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I’ll discuss how to run end-to-end tests with Claude Code, why you should be running end-to-end tests, and specific techniques you can use to make end-to-end testing effective.

Why run end-to-end tests with Claude CodeFirst of all, I’d like to cover why you should care about this topic.

The reason you should run end-to-end tests with Claude Code is simply that it’s an effective way of testing your code implementations.

How to run end-to-end tests with Claude CodeNow it’s time to discuss how to actually run end-to-end tests.

Thus, I’ll cover some different techniques I use to run end-to-end testing with Claude Code that make my end-to-end testing more effective.

Article 8A (the answer contract) declared the typed answer schema; Article 8B (prompt assembly) built the dispatcher that calls the model against it.

1.4 “Not found” as a first-class outputIn enterprise RAG, returning the wrong answer is worse than returning no answer.

A “not found” forces the user to look further, which is the right behavior when the system doesn’t have the answer.

if provider == "openai": return _openai_call(system, user, schema, model, temperature) if provider == "anthropic": return _anthropic_call(system, user, schema, model, temperature) if provider == "ollama": return _ollama_call(system, user, schema, model, temperature) if provider == "mistral": return _mistral_call…

Effect Importance 95% Bootstrap CI Triple interaction (Model × Prompt × Tool) 0.31 [0.023, 0.635] Model (main) 0.25 [0.018, 0.572] Model × Prompt 0.15 [0.009, 0.528] Tool (main) 0.13 [0.006, 0.451] Prompt × Tool 0.07 [0.006, 0.319] Prompt (main) 0.03 [0.005, 0.338] Model × Tool 0.01 [0.004, 0.341]In this particular experiment, the biggest signal is the three-way interaction between model, prompt, and tool.

What makes it stand out is that the model, prompt, and tool reinforce each other.

The weaker model may need the scaffold and semantic tool more, while the stronger model may experience the same scaffold as friction.

In this setup, the Systematic Planner and semantic tool appear to create …

From brief to prompt: the dispatcherOne prompt per question shape, composed at call time.

Contract: a ParsedQuestion comes in; three things come out: the schema (picked from ANSWER_REGISTRY by expected_answer_shape ), the system prompt (a fixed BASE plus the fragments the brief requests), and the user prompt (question + keywords + labeled passage lines).

The schema and the dispatcher get promoted to the package once the question parsing brick lands its full implementation.

1.3 The system prompt: BASE + fragmentsThe BASE is shape-agnostic: it encodes the contract that holds for every call: cite, type, fail honestly.

Spelling it out in the system prompt and labeling the columns in the user pr…

I wrote about the FPN (Feature Pyramid Network) architecture [1], which is one of the most influential necks we can apply to a backbone model.

And here’s the problem FPN doesn’t solve: while FPN effectively enriches feature maps in shallower layers, the deeper feature maps still lack spatial information.

PANet From ScratchThe idea of our implementation here is that we will create a dummy backbone with an FPN and place the PANet layers on top of it.

# Codeblock 7 panet = PANet() out_panet = panet(p2, p3, p4, p5) n2, n3, n4, n5 = out_panetAnd below is what the output looks like.

# Codeblock 7 Output n2 : torch.Size([1, 256, 56, 56]) n2 downsampled : torch.Size([1, 256, 28, 28]) after sum : to…

I’m going to start by asking my local model what it thinks about open source models:Answer from the Local Model (Thinking Tokens)The light grey text represents the model’s internal reasoning process.

Here are three common and practical uses for a **local LLM (Large Language Model)**: 1.

**Content Creation and Language Processing** You can use a local LLM to generate creative content such as blog posts, stories, scripts, or marketing copy.

You can now create your own applications with your own local model quite easily.

Here is how you bypass the reasoning pass:Disable it entirely: ollama run qwen3:8b --think=falseRun it, but hide it from the UI: ollama run qwen3:8b --hidethinkingIn scripts: …

This is the first of its three parts: the contract, the typed answer schema the model has to fill.

Asking the model for more than “the answer”The schema is the contract between the pipeline and the model, and it doesn’t have to stop at “the answer”.

Schema (or Answer) = the top-level Pydantic class passed to responses.parse ( AmountAnswer , TextAnswer , …); inherits AnswerBase for the shared feedback fields.

2.1 Typed values, one schema per answer_typeThe first layer of the schema is the value: the typed primitive the model fills in.

The citation-bearing answer schema ( AnswerWithEvidence ) is in the same family as Bohnet et al.

This is precisely the kind of dependency that parallel tool calling can’t resolve in one round, and exactly what a ReAct loop is built for.

And now that we have set up everything, we can finally see the ReAct loop in action.

This is the major differentiation (at least for this simple scenario) between parallel tool calling and the ReAct loop.

On my mindSo, when does a ReAct loop actually beat parallel tool calling?

This ReAct loop, in one form or another, is the actual mechanism behind most of what people mean by an “agent”.

What do 512 tokens vs. 8192 tokens actually cost you — in training time, inference time, on GPU, and on CPU?

What do 512 tokens vs. 8192 tokens actually cost you — in training time, inference time, on GPU, and on CPU?

2.1 Reaching a long context windowA standard transformer works by having every token attend to every other token.

That quadratic scaling is why long context is costly, and on a smaller GPU, sometimes just not possible.

A careless cut can throw away exactly the thing a long context window would have preserved.

So I replaced the entire pipeline with a pure Python compiler.

Unlike an LLM which varies its output, a compiler gives you the same result every single time you run it.

Once you strip away the LLM calls, what’s actually left to do is text parsing, string manipulation, and graph traversal over an in-memory dictionary.

Nothing had changed in the source files.

## Notes _(add your own notes here -- preserved on recompile)_Nothing in the raw file told the compiler that Gradient Descent references this page.

It zooms in on brick 3 (retrieval) of the four-brick architecture and surfaces the lessons most tutorials skip.

where this article sits in the series: brick 7 (retrieval) highlighted – Image by author📓 Runnable companion notebooks are on GitHub: doc-intel/notebooks-vol1.

Keywords always run, the TOC always reasons, embeddings fire only when the vocabulary mismatches – Image by authorBelow are the six untaught lessons of this brick.

The shift is simple to state: the question has columns, the document has columns, and retrieval is the join.

Series retrieval returns anchor + scope as a typed pair.

I say she tries to be mathematically excellent, which involves promoting mathematical excellence through mathematical excellence, and that this structure is closely related to why 'mathematical excellence' can even be a concept.

This is not to suggest that ‘good mathematics causes future good mathematics’ is a full definition or even full informal description of good mathematics.

My claim is only that the fact that good mathematics has a disposition to cause future good mathematics reveals something essential about our concept of good mathematics (and about the material affordances enabling this concept).

‘mathematical excellence through mathematical excellence’ AI?

I believe ‘mathematical …

The opinion section, discusses that domains are a good fit for rapid progress of AI models vs which ones are challenging.

Subscribe and don’t miss out:📝 Editorial: Fable 5's Comeback, ZCode's Debut, Claude Science, and the $3.5B Deployment Land GrabThis week’s developments in AI provide a clear direction where the space is going: more capable models and the imperative of capable delivery capabilities.

Start at the model layer, where we just watched the first frontier model get hot-patched by a government.

One layer up, Anthropic launched what I’d call claude --science .

Claude ScienceAnthropic announced Claude Science, a workbench for scientists.

The core thesis of this essay is simple to state: space is becoming a new frontier for AI — and one of the most competitive ones.

When the scarce thing was ideas, the frontier was architectures; when it was data, the frontier was the open web; when it was FLOPs, the frontier was the fab.

Today the scarce thing is energy — grid capacity, cooling water, land, permits — and orbit is the one place in reach where energy is effectively unmetered and no zoning board has jurisdiction.

This essay discusses the core thesis of AI in space: value proposition, key players, architecture differences and much more.

The core thesis: compute is now an energy problem, and space is an energy solution

Today, we are covering an amazing paper published by Meta last week: https://arxiv.org/abs/2606.25996There is a quiet shift happening in AI training.

For years, the center of gravity was the model: more parameters, more GPUs, better architectures, longer context windows, better optimizers.

Meta’s new Autodata work flips that perspective.

Not “ask a strong model to generate a million examples and hope the distribution is useful.” Instead, Autodata treats data generation like a miniature research loop.

An AI agent creates examples, tests them, studies the failures, updates its recipe, and tries again.

The simplest way to understand knowledge distillation is to imagine a very expensive teacher and a very cheap student.

The teacher is a large model: smart, slow, high-capacity, expensive to run.

The student is smaller: faster, cheaper, easier to deploy, but usually less capable if trained in the standard way.

Distillation asks a very practical question:Can the student learn not only from the original dataset, but from the teacher’s behavior?

In other words, instead of training the small model directly on reality, we train it on reality as interpreted by the big model.

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: Models, Games, and the Future of EvaluationThis week in AI had the strange feeling of a stack trace resolving itself.

Frontier AI releases are starting to look less like software updates and more like controlled deployment of critical infrastructure.

Then came General Intuition’s new raise, which feels like the cleanest signal yet that the next data frontier is not text, or even video, but action.

AI Lab: Qwen TeamSummary: This research introduces Qwen-AgentWorld, a foundational language world model designed to simulate seven diverse agentic environments through long chain-of-thought reasoning.

🤖 AI Tech ReleasesGPT 5.6 SolOpenAI un…

The scientist decides what to test, transfers samples between instruments, inspects the results, updates their mental model and chooses the next experiment.

A self-driving lab moves part of that loop into software.

It makes something, measures it, updates a model and chooses the next move.

A self-driving lab can run the first few hundred experiments, notice that most of the remaining design space looks unpromising and redirect itself toward better candidates.

The simplest mental model is a loop:design → make → test → learn → design again

For about three years now, the Qwen family has lived inside a rectangle.

It reads your code, looks at your screenshots, answers your questions, and the whole time it has been doing this behind glass.

The Tongyi Lab team put it plainly: seeing is not acting.

Let me explain why I think this is the right shape, and where I’d keep my skepticism.

The actual bottleneck is not intelligence, it’s tokenization

I am super excited about this series that deep dives into distillation techniques.

Bigger models.

The frontier model became one of the strangest artifacts in the history of computing: a single neural network that looks less like a program and more like a compressed civilization of patterns.

A coding agent does not always need a frontier model for every token.

It may need a smaller draft model, a specialized debugging model, or a distilled planner trained on expert trajectories.

A fun, personal note to start the week — about AI evaluations, and why we made the best models in the world fight over a virtual ball.

LayerLens builds the evaluation and observability layer for that world, working alongside frontier AI teams to ship benchmarks that probe what the standard suites miss.

Introducing the Stratix CupToday, LayerLens is launching the Stratix Cup — a soccer (football, if you insist) tournament in which the top frontier models compete against each other inside a harness that simulates a full soccer environment.

The format is straight out of the World Cup playbook: 16 models, four groups of four, group stage into knockouts, all the way to a single final.

Here’s GLM…

A week of really unexpected turns in the AI market.

AI tokens are becoming are incresingly becoming part of every company economics.

You see large companies measuring and reporting token expenses and forecasts like a well understood accounting units.

In reality, we are entering a new era: the token economy.

An ERP for tokens?

The Smallest Billable ThoughtSomething strange happened quietly, and then all at once: the token became the atomic unit of the AI economy.

As we wrap up our series about alternatives to transformer architectures, Google DeepMind just released one of the most impressive models in this category.

DiffusionGemma is a text-diffusion model that challenges the conventional transfromer models.

Most language models write like a typewriter.

This architecture has carried the entire modern LLM era: GPT-style chatbots, coding copilots, reasoning models, agent frameworks, enterprise assistants.

Google’s new DiffusionGemma asks a deceptively simple question: what if text generation did not have to work that way?

The Transformer didn’t win because it was the most elegant or the most brain-like design.

Every token looks at every other token, the whole thing maps cleanly onto a GPU grid, and you train it all at once.

The cost is that attention scales quadratically with sequence length, and autoregressive decoding drags around a KV-cache that grows linearly with every token you’ve already seen.

The second family is state space models — the SSM/Mamba line, the most serious challenger of the bunch.

The third family is text diffusion — generation that abandons left-to-right decoding entirely, refining a whole sequence in parallel over a handful of denoising steps.

Subscribe and don’t miss out:📝 Editorial: Last Week in AI: Anthropic Ships, Apple Borrows, Musk Lists, Bezos BuildsSome weeks in AI feel like incremental patch releases.

🔎 AI ResearchAI Lab: Google Research & Google DeepMindSummary: This paper introduces a unified framework for constructing practical, kernel-based two-sample tests derived from the family of f-divergences.

AI Lab: Stanford UniversitySummary: DELM is a novel multi-agent framework that eliminates the bottleneck of centralized orchestration by relying on a shared, verified context and an asynchronous task queue.

🤖 AI Tech ReleasesClaude Fable 5 and Mythos 5Anthropic released its highly anticipated Fable 5 model, a limited Mytho…

Но это не вайбкодинг, а тяжёлая профессиональная ИИ-разработка.

За это время по этому проекту в ChatGPT было создано 112 чатов — это примерно 560 промптов.

И в особо напряжённые периоды приходилось вставать по ночам, чтобы оптимально использовать лимиты, которые делятся на 5-часовые и недельные сессии.

Но это не магия и не кнопка «сделать хорошо».

Именно поэтому будущее не за вайбкодингом, а за теми, кто научится управлять этой скоростью.

Простой пример с ценами на топливо: бензин дорожает и из-за роста цены на нефть, и из-за ее падения.

Осознание того, что твой труд увеличивает чью-то капитализацию, но не решает реальных проблем общества, видимых в быту и в новостях, подтолкнуло искать еще какую-то деятельность.

Кроме того, благодаря АМБ появился уникальный датасет новостей с противоречиями современного общества на kaggle и github, далее о нем.

Датасет новостей о противоречиях современного обществаАктивисты АМБ и волонтеры дружественных коллективов собрали и разметили датасет новостей, подсвечивающие те самые системные противоречия, о которых я задумывался ранее.

Пример Б В 2023 году в мире голодал каждый 11-й человек, а в …

Подробный разбор того, как команда OpenAI Codex создаёт своего кодового агента, как его используют инженеры и что это может значить для будущего разработки ПО.

Чтобы разобраться, как устроен Codex, как команды внутри OpenAI его используют и как он влияет на инженерные практики у создателей ChatGPT, я поговорил с тремя сотрудниками OpenAI:Тибо Соттио (Thibault Sottiaux) — руководитель Codex.

Оба продукта были запущены весной: Codex CLI анонсировали в апреле 2025 года, а Codex в ChatGPT представили в мае.

В команде Codex эти файлы объясняют агенту, как ориентироваться в кодовой базе, какие команды запускать для тестирования и как следовать стандартам проекта.

Использование Codex в OpenAIПомим…

10 февраля мы в очередной раз запускаем бесплатный онлайн-курс по обработке естественного языка (Natural Language Processing).

Что будем проходить:классическое начало: закон Ципфа, TF-IDF, RNN, CNN, Transformer;основные задачи NLP: классификация текста, тегирование и генерация;специфичные области: агенты и вайб-кодинг;LLM и их применение.

Если вы студент ИТМО, МФТИ или ВШЭ, то курс можно зачесть, как учебный.

Работаю в области NLP более 12 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

Если есть вопросы, то приходите с ними в ODS Mattermost – там будут все ответы, время семинаров и ссылки.

Однако все задачи в MERA CODE, как впрочем и в SWE-bench и других бенчмарках подобного назначения, следуют классической парадигме, когда у нас есть фиксированный обучающий набор данных и, что более важно, фиксированный проверочный набор.

Но большие языковые модели для кодинга, которые мы и пытаемся оценивать нашим набором, также учатся на GitHub – со времен еще первой модели LLaMa.

Кажется, что 700 задач немного, но это уже очень приличное количество, и что самое важное — это новые задачи.

Current behavior: from sympy import ask, Q, Symbol x = Symbol('x') print(ask(Q.finite(x**-1), Q.real(x))) # Output: True Expected behavior: The function should return None to indicate uncertainty, as x**-…

tools = tools self .

tools = tools self .

threshold : return { "status" : "resolved" , "tool" : tool [ "name" ] , "confidence" : score , "attempts" : 1 } # Reformulate by stripping filler words.

tools = tools self .

full_catalog_tokens = sum ( estimate_tokens ( d ) for d in descs ) def _retrieve ( self , query : str , top_k : int ) -> list [ dict ] : query_vec = self .

Share Post ShareIn this article, you will learn how context engineering and memory engineering solve different problems in agentic AI systems, and how the two disciplines meet at the point where retrieved memory enters the context window.

Most of the time, the problem lies in two areas that get built together, conflated, or skipped: context engineering and memory engineering.

Memory Engineering: Designing Persistent AI Memory SystemsOnce an inference call completes, memory engineering determines what deserves to persist and under what conditions it gets used again.

trust_level >= 0.5 )AI Agent Memory Design Guide – Working, Long-Term, and Procedural Memory with Forgetting and Staleness Mana…

Share Post ShareIn this article, you will learn five practical strategies for managing context windows in long-running AI agent applications, along with the key tradeoffs each approach introduces.

Five distinct context management strategies: sliding windows, recursive summarization, structured state management, ephemeral context via RAG, and dynamic context routing.

Accordingly, shifting from “LLMs as prompt-response engines” to “(agent-endowed) LLMs as long-running background processes” turns context windows into a major AI engineering bottleneck.

For all these reasons, managing context windows in the long run requires specific strategies like sliding windows, tiered memory, and dynamic su…

Share Post ShareIn this article, you will learn how the Model Context Protocol (MCP) standardizes the way AI applications connect to external tools and data sources, broken down across three levels of depth.

How the host, client, and server work together, and what happens when a model’s request flows through an MCP server.

The transport options, security risks, and deployment choices that matter once an MCP server is running in production.

Accessing information outside that context requires external tools.

Because both sides follow the same protocol, an MCP server can be used by any compatible MCP client without requiring a custom integration for that specific client.

According to Atlan’s research on AI agent memory, 95% of enterprise generative AI pilots delivered zero measurable ROI in 2025, with failure attributed to context readiness rather than model quality.

isoformat ( ) , "user" : user_input , "agent" : agent _ response } ) def load_recent_episodes ( n : int = 5 ) -> str : "" "Retrieve the last N episodes as a formatted string for injection into context."

Prerequisites:pip install langchain langchain-openai langchain-chroma langchain-text-splitters chromadb python-dotenv 1 pip install langchain langchain - openai langchain - chroma langchain - text - splitters chromadb python - dotenvHow to run: Save as rag_pipeline.py, add OPENAI_API_KEY to your…

lower ( ) : return "billing" return "general" def extract ( raw_input : str ) -> str : "" "Step 1 -- always runs, always leads to step 2.

def handle_billing(text: str) -> str: return f"[BILLING TEAM] Routed: {text[:50]}" def handle_technical(text: str) -> str: return f"[TECH SUPPORT] Routed: {text[:50]}" def handle_general(text: str) -> str: return f"[GENERAL QUEUE] Routed: {text[:50]}" # The branch map IS the entire decision space.

def handle_billing ( text : str ) -> str : return f "[BILLING TEAM] Routed: {text[:50]}" def handle_technical ( text : str ) -> str : return f "[TECH SUPPORT] Routed: {text[:50]}" def handle_general ( text : str ) -> str : return f "[GENERAL QUEUE] Routed: {text…

Topics we will cover include:Why a context window behaves like a stateless scratchpad rather than persistent memory.

Deeming a huge context window as “memory” is, in architectural terms, similar to buying a 25-foot-wide office desk because you are reluctant to acquire a filing cabinet.

Context WindowA context window in an AI model, particularly agent-based ones with underlying language models, is like a desk surface or a stateless scratchpad.

When passing an agent a conversation history spanning over 200K tokens (large context window), it isn’t remembering what happened at a previous step in time.

Generate a compact summary for the active prompt summary = summarizer_model.generate(raw_trans…

Share Post ShareIn this article, you will learn how to build a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based clustering algorithm, to automatically discover topics in unlabeled text data.

target } ) df = df [ df [ 'text' ] .

) print ( "Sample document:" ) print ( df [ 'text' ] .

cluster import HDBSCAN # Initializing HDBSCAN # min_cluster_size=8: we specified that each cluster must have at least 8 documents clusterer = HDBSCAN ( min_cluster_size = 8 , min_samples = 3 , store_centers = 'centroid' ) df [ 'cluster' ] = clusterer .

shape [ 1 ] ) ] ) reduced_df [ 'cluster' ] = df [ 'cluster' ] # Getting all unique pairwise combinations of the …

# Prerequisites: pip install playwright && playwright install chromium # How to run: python scrape_books.py import asyncio import json from playwright .

"" page = await get_page ( ) await page .

"" page = await get_page ( ) await page .

"" page = await get_page ( ) await page .

"" page = await get_page ( ) try : await page .

Step 1: Understanding Why Agent Evaluation Is ImportantThe instinct when an agent fails is to treat it as a prompting problem: the system prompt needs to be clearer.

Step 2: Defining What Agent Evaluation Success Looks LikeEvaluation is only as good as its success criteria.

Step 4: Grading Agent Reasoning and Output Quality with Model-Based JudgesSome agent evaluation dimensions resist deterministic checking — output quality, tone, faithfulness to retrieved context, appropriate empathy.

Step 5: Matching Agent Evaluation Strategy to Agent TypeGrading strategies apply broadly, but agent type determines which graders carry the most weight and which failure modes to prioritize.

Summary of Key A…

How to build, run, and evaluate a zero-shot sentiment classification pipeline using scikit-learn-compatible syntax.

from sklearn.pipeline import Pipeline from skllm.models.gpt.classification.zero_shot import ZeroShotGPTClassifier # Define the end-to-end pipeline sentiment_pipeline = Pipeline([ ("cleaner", text_cleaner), # Updated to use Groq's active Llama 3.1 8B model ("llm_classifier", ZeroShotGPTClassifier(model="custom_url::llama-3.1-8b-instant")) ]) # Fit the pipeline # Note: For Zero-Shot classification, fit() doesn't train the LLM.

pipeline import Pipeline from skllm .

. . Actual : negative | Predicted : negative Review : This entry is certainly interesting for series fans ( like mys…

What Works in AI Agent Tool Design1.

The difference becomes clearer when comparing a multi-action tool against dedicated single-purpose tools:# Avoid: action-based multi-behavior tool @tool def manage_customer( action: str, customer_id: str | None = None, data: dict | None = None ): """ action: create | get | update | delete | suspend """ ... # Prefer: single-responsibility tools @tool def create_customer(data: CustomerInput) -> Customer: """Create a new customer record."""

... @tool def suspend_customer(customer_id: str, reason: str) -> SuspensionResult: """Suspend a customer account."""

. . # Prefer: single-responsibility tools @ tool def create_customer ( data : CustomerInput ) -> Custom…

Share Post ShareIn this article, you will learn five essential Python concepts that every AI engineer must master to build scalable, production-grade AI systems.

training = True def __call__ ( self , x ) : return [ val * 1.5 for val in x ] class InferenceProfiler : def __init__ ( self , model ) : self .

model = model def __enter__ ( self ) : self .

) return self def __exit__ ( self , exc_type , exc_val , exc_tb ) : # Restore the original training state self .

training = self .

Share Post ShareIn this article, you will learn how to perform multi-label text classification using large language models and the scikit-LLM library, without the need for labeled training data or complex model training.

Topics we will cover include:What multi-label classification is and why it matters for nuanced text analysis.

And that’s precisely what we will do: load, adapt, and leverage a pre-trained LLM for a multi-label classification task where a piece of text can be assigned one or multiple categories.

to_pandas ( ) # Extract the raw text comments texts = df [ 'text' ] .

Wrapping UpThis article illustrated how to conduct a multi-label text classification process with scikit-LLM: a …

. . < / div > < div class = "tabs" > < div class = "tab active" data - tab = "file" > Upload File < / div > < div class = "tab" data - tab = "mic" > Record Microphone < / div > < / div > < !

. . < / div > < / div > < / div > < !

< / div > < div class = "tabs" > < div class = "tab active" data - tab = "image" > 🖼 Image Analysis < / div > < div class = "tab" data - tab = "speech" > 🎙 Speech Transcription < / div > < / div > < !

. . < / div > < / div > < / div > < !

all ( [ pipeline ( 'image-classification' , 'Xenova/vit-base-patch16-224' , { dtype : 'q8' , progress_callback : p = > p . status === 'done' && markReady ( 'badge-cls' , 'Classifier' ) } ) , pipeline ( 'image-to-text' , 'Xenova/vit…

People keep talking about how AI will make their job easy, and I don’t really understand why.

I assume the factory job producing this was still hard work.

I don’t think AI has made my job chill, and I feel like I am front-line compared to much of the economy.

It’s not widely known, but transportation and warehousing has the highest rate of nonfatal work injuries in the US.

For a while, this will not lead to any job loss, because increasing abundance will lead to higher demand.

On Monday, Anthropic filed a lawsuit against the Department of War, and an amicus brief in support of Anthropic was filed on behalf of a number of OpenAI and Google employees.

There’s also an amicus brief filed on behalf of Microsoft.

There’s conflicting reporting, but very broadly, Anthropic signed an agreement with the government to deploy Claude in classified, military contexts.

Anthropic said no, Pete Hegseth declared them a supply chain risk, and Anthropic filed a lawsuit against this.

The amicus brief was broadly aligned with my thoughts on the matter, so I signed.

This has spoilers for MIT Mystery Hunt 2026.

Pre-HuntThe time running up to Hunt was more stressful than usual…very briefly, I typically hunt with teammate.

Just last year, I did GPH 2025, LN Hunt, Teammate Hunt 2025, Microsoft Hunt 2025, and Silph Puzzle Hunt 2025, all of which had significant 3+ hour solve puzzles that would not be out of place in Mystery Hunt.

Not to mention smaller hunts like Advent Hunt, and then I didn’t even do Brown Puzzlehunt or Vertex Hunt or the fall CMU Hunt.

To me, the crux is whether Mystery Hunt is broken, or Mystery Hunt is fine.

* * *I’ve been thinking about the anger surrounding generative AI.

To keep things fair, he took the best human images and best AI images, meaning human art from famous artists, and AI art from prompters skilled at removing obvious tells of image generation.

When people complain about AI slop, I see it as a complaint against the deluge of default style AI images.

We’ve seen this happen in all forms: AI text, AI music, older forms of computer generated content like CGI.

As much as we celebrate imperfection, digital imperfection is a step too far.

February 25, 2026The Future of SoftwareThe world of software is undergoing a shift not seen since the advent of compilers in the 1970s.

How will humans tell AI agents what software artefacts we would like to create?

How will humans tell AI agents what software artefacts we would like to create?

This future of software creation, in which our programming languages are abstracted away, raises two very important questions:What will the instruction/specification language look like?

This should be a clear layer of separation between the developer and the pool of AI agents working to maintain software.

Deep Learning is Powerful Because It Makes Hard Things Easy - Reflections 10 Years OnTen years ago this week, I wrote a provocative and bold post that blew up, made it to top spot on HackerNews.

In hindsight: There is a lot of stuff in deep learning that we don't understand nearly enough.

Sometimes things work for reasons completely unrelated to why we thought they would work.

(Pop some 🍿 in the microwave and read till the end for more)🎯 "Deep learning is powerful exactly because it makes hard things easy"Okay, this was a great insight.

🎯 Generative ModelingIn the post I suggested people learn "something harder" instead of - or in addition to - deep learning.

its children def __add__ ( self , other ): other = other if isinstance ( other , Value ) else Value ( other ) return Value ( self .

data , ( self , other ), ( 1 , 1 )) def __mul__ ( self , other ): other = other if isinstance ( other , Value ) else Value ( other ) return Value ( self .

extend ( head_out ) x = linear ( x_attn , state_dict [ f 'layer { li } .attn_wo' ]) x = [ a + b for a , b in zip ( x , x_residual )] # 2) MLP block x_residual = x x = rmsnorm ( x ) x = linear ( x , state_dict [ f 'layer { li } .mlp_fc1' ]) x = [ xi .

Since microgpt processes one token at a time (no batch dimension, no parallel time steps), we build the KV cache explicitly.

The model is completing the document…

Uploading audio references and scene extension is not yet supported in the Gemini API for this model.

Video references up to 3 seconds in duration are accepted by the API schema but are not correctly processed by the model at this time.

Gemini Omni is available in public preview starting today in Google AI Studio and the Gemini API.

Use Nano Banana 2 Lite as a high-speed image generation model, then pass that image as a reference to Gemini Omni Flash to animate it into a high-quality video.

To help you get started we created a few demo apps you can remix that let you experience how you can pair both Nano Banana 2 Lite and Gemini Omni Flash into one workflow.

Making computer use safe in 3.5 FlashTo mitigate some of the prompt injection risks for agents operating in live environments, we use targeted adversarial training for computer use in Gemini 3.5 Flash.

We’re also releasing two optional enterprise safeguard systems that enable enterprises to:Require explicit user confirmation for sensitive or irreversible actions.

Automatically stop tasks if an indirect prompt injection is identified.

Taking a “defense-in-depth” approach, we encourage developers to combine these features with secure sandboxing, human-in-the-loop verification and strict access controls.

We are already seeing customers drive value with computer use.

New UK government AI planning prototype built with Gemini aims to halve the time it takes to process homeowner applicationsAround the world, Governments are exploring how AI can deliver better public services, faster.

The UK is working to build 1.5 million new homes by 2029, but local planning authorities are often slowed down by dense paperwork and administrative backlogs.

To help get Britain building, we’re partnering with the UK government to help radically shorten the time it takes to process householder planning applications.

Following early trials in Barnet, Camden and Dorset, the government plans for the new AI planning tool to be made available to all councils nationally from 2027.

…

How we’re securing internal systems against increasingly capable and imperfectly aligned AIAI agents are transforming our relationship with technology.

In the U.S alone, AI agents could create $2.9 trillion in economic value by 2030.

That’s why we developed our AI Control Roadmap: a framework for building and managing the advanced AI we deploy within Google.

Similarly, our AI control system grants AI agents permissions based on their verified behavior, allowing us to build trust through controlled, incremental access.

In our AI Control Roadmap, we map security protocols to measurable milestones in AI capabilities on two critical fronts:

While the AI research community has explored diffusion-based text generation for years, applying it to large models has remained a challenge.

DiffusionGemma changes this by shifting how models use hardware.

But when run locally for a single user, this word-by-word process leaves your dedicated GPU or TPU underutilized — it spends most of its time simply waiting for the next "keystroke."

By giving the computer's processor a larger chunk of work at once, DiffusionGemma utilizes your hardware to its full potential.

It upgrades your model inference from a single, sequential typewriter to a massive printing press that stamps the entire block of text simultaneously.

Scaling AI Safety Research for a Multi-Agent WorldFor the past decade, we’ve focused on making individual AI models more capable, helpful and safe.

The funding call focuses on the study of how large-scale multi-agent AI systems behave as a group, and how we can provide frameworks to understand and mitigate against potential risks.

Scaling the frontier of multi-agent safety researchAlthough foundational frameworks for multi-agent safety exist, the rapid evolution of these systems requires an immediate, large-scale expansion of research.

A collaborative call to actionNo single lab can solve multi-agent safety alone.

Building realistic, reproducible environments to evaluate, compare and accele…

Today, we’re taking our next step with the release of Gemini 3.5 Live Translate, our latest audio model for live speech-to-speech translation.

The model automatically detects 70+ languages and generates smooth, natural-sounding translated speech that preserves the speakers' intonation, pacing and pitch.

Unlike turn by turn systems that wait for the speaker to finish speaking before responding, 3.5 Live Translate generates speech continuously, balancing the trade-off between waiting for context to improve quality and translating immediately to stay in sync with the speaker.

Gemini 3.5 Live Translate is rolling out starting today across Google products:For developers in public preview via the…

Today, we are introducing Gemma 4 12B, our latest model designed to bring agentic multimodal intelligence directly to laptops.

Here’s an overview of what makes Gemma 4 12B unique:Novel unified architecture: No multimodal encoders.

Advanced reasoning: Benchmark performance nearing our 26B model, unlocking powerful multi-step reasoning and agentic workflows.

Benchmark performance nearing our 26B model, unlocking powerful multi-step reasoning and agentic workflows.

Small enough to run locally on consumer laptops with 16GB of RAM, it unlocks powerful multimodal and agentic experiences right on your machine.

That’s why we’re launching the Google DeepMind Accelerator: Robotics, a three-month program for early-stage robotics startups across Europe.

They’ll have access to our AI stack, technical expertise and Gemini robotics models.

Extend Robotics ( United Kingdom ): Provides teleoperation software and data pipelines that help train and fine-tune foundation models for real-world robotics applications.

Generative Bionics ( Italy ): Amplifies human potential by developing humanoid robots based on physical AI, developed in Europe but built to scale globally.

): Amplifies human potential by developing humanoid robots based on physical AI, developed in Europe but built to scale globally.

The results from this pre-registered trial suggest that AI can be a powerful pedagogical partner — not by replacing teachers, but by augmenting their reach.

Students using Guided Learning saw a gain of +0.258 standard deviations in their math scores compared to the control group.

In practical terms, this represents roughly 1.2 to 1.7 years of typical learning progress achieved within the eight-week trial.

To further understand the impact of Guided Learning on student learning, we are conducting a series of additional pre-registered RCTs globally.

Additionally, our support of the Global AI for Learning Alliance (GAILA) will accelerate these commitments and others through collective action.

The Asia-Pacific region is a global engine for economic growth, but it's also highly vulnerable to climate change.

While green technologies are gaining momentum, a recent report shows they aren’t scaling fast enough to keep up with the region’s rising environmental risks.

Selected organizations will receive expert mentorship, tailored support and help integrating frontier AI and science AI models from Google AI experts into their projects or products.

If you're working on climate solutions, we want to help you scale your work.

The program kicks off with an in-person bootcamp in Singapore, and you can learn more and register your interest today.

Biologists Omar Abudayyeh and Jonathan Gootenberg are using Co-Scientist to help them blast through both.

Their lab runs huge genetic screens that flip thousands of genes on or off then reads how cells respond to these changes.

Co-Scientist is helping on two fronts.

Second, Co-Scientist speeds up the follow-through.

Having Co-Scientist analyse their screening data alongside the literature, that work is slashed to just a few days.

Street View: ground your worlds in real placesWhen creating imaginative worlds in Project Genie, you can now also base them on real places.

This capability is powered by Maps Imagery Grounding, the same technology developers use to create stunning AI visuals with Street View.

Street View imagery in Project Genie is available now for places in the U.S. with plans to expand to more places over time.

Project Genie: now available with Google AI UltraStarting today, Project Genie — including the new Street View capability — is gradually rolling out to all eligible Google AI Ultra $200 subscribers globally (18+).

Try creating today with Project Genie.

We’re introducing Gemini Omni, where Gemini’s ability to reason meets the ability to create.

Omni is our new model that can create anything from any input — starting with video.

With Omni, you can combine images, audio, video and text as input and generate high-quality videos grounded in Gemini's real-world knowledge.

Today, we’re rolling out the first model in the Omni family: Gemini Omni Flash, to the Gemini app, Google Flow and YouTube Shorts.

Here’s some of what makes Omni special:Edit your videos through conversationGemini Omni gives you an easier way to edit video — with natural language.

The era of the traditional connected vehicle has shifted into the age of the software-defined vehicle (SDV), notable for rapid innovation with many new capabilities delivered over the air.

To better support and secure SDVs, Google Cloud and Valtech have partnered to develop Nexus SDV, a highly-scalable, AI-enabled connected vehicle platform built on Google Cloud.

Cloud-native under the hoodThe architecture of Nexus SDV is built on a modular, cloud-native foundation designed to bridge the gap between the vehicle edge and the data center.

This robust data loop allows Nexus AI to quickly push intelligent updates and services back to the vehicle.

By providing this developer-friendly, open frame…

Here is a sample result of summarized reviews for two gaming console products:productname reviews_summary AlphaCore Console Users praise the stunning 4K graphics, smooth 120Hz frame rates, and the highly ergonomic controller design.

We have shattered these barriers by introducing two breakthrough capabilities:Smart Batching for AI Functions: This AI Function Acceleration capability provides intelligent batching of AI function calls for optimal performance and quality.

That’s because, AlloyDB intelligently determines the right batch size for optimal results - if you underestimate the batch size, you won’t reap gains for cost and latency, and if you overestimate the batch size, the prompt to …

Anthropic's agentic coding tool Claude Code has worked with Google Cloud for a while now.

An individual developer could easily point CLAUDE_CODE_USE_VERTEX=1 at a Google Cloud (GCP) project, grant the role roles/aiplatform.user , and inference stays inside your Google Cloud perimeter.

It is a self-hosted service, shipped with the same claude binary, that sits directly between your local Claude Code clients and Google Cloud.

This post breaks down exactly why you should run it and what a secure deployment looks like on Google Cloud.

(Note: If you want to jump straight to the code, the full walkthrough lives in the Claude apps gateway on Google Cloud docs.)

Today, we’ll dive into how to use the Gemini Enterprise Agent Platform remote MCP server to securely connect your external AI agents to the resources inside your Google Cloud environment.

Connect your IDE to Google CloudThink of the Agent Platform MCP server as a bridge between your favorite external development tools and your Google Cloud architecture.

If you are building an agent in Antigravity CLI or Claude Code, for example, the Agent Platform MCP server allows that agent to securely interact with your Agent Platform resources.

The Agent Platform MCP server provides a single, standardized interface for your external agents so you can spend less time writing integration code and more tim…

When it comes to modern drug discovery and materials design, though, there’s demand for even faster processing speeds to handle massive chemical libraries involved.

Schrödinger's primary technical goal was speeding up AI model training for energy and force calculations.

Specifically, they targeted the Ewald summation, a critical but computationally demanding function used in molecular mechanics.

By incorporating AlphaEvolve into their models, the system could generate a batched implementation of the Ewald summation using parallel batch matrix multiplication.

This acceleration lets researchers compress molecular screening timelines and directly benefits several key research areas:Drug discov…

To help you create rich, reliable experiences while reducing regeneration time and costs, we’re adding two new models to Gemini Enterprise Agent Platform.

First, we’re announcing the general availability of Nano Banana 2 Lite (Gemini 3.1 Flash-Lite Image).

This model is the fastest and most cost-efficient image generation and editing model within the Nano Banana model family.

We’re also releasing Gemini Omni Flash in public preview.

Grounded in Gemini's real-world knowledge, it powers high-quality video generation and conversational editing.

Centralized AI code scanning and the Mantis frameworkNaive, decentralized AI code scanning suffers from sloppiness, frequently hallucinating bugs and yielding true-positive rates under 7%.

To solve this, we built Mantis, our core multi-agent orchestration framework designed specifically for scalable, context-aware repository analysis.

The core skills at the heart of Mantis are now open source to demonstrate the fundamental concept.

Research agents : Acting as specialized domain investigators, these agents use internal code searches to drill into raw source files, examining data tracking, control flows, and sanitization logic.

Self-healing fuzz testingWhile code scanning provides breadth, dy…

While BigQuery already offers powerful AI functions that help you analyze individual rows of data, analyzing unstructured data at scale requires a different approach.

AI.AGG() lets you ask questions from unstructured data such as logs and documents, for example:What are the top three feature requests among the negative product reviews?

Analyzing system logs with AI.AGG()A great example of the power of AI.AGG() is analyzing system logging.

In fact, our BigQuery engineering team used this exact approach while developing AI.AGG() — using the function to help identify edge cases related to input handling for the feature itself!

You can load the sample data file into BigQuery using any of the su…

To help organizations confidently deploy these workflows, we recommend VPC Service Controls (VPC-SC) to establish an essential network-level, destination-based perimeter.

What's new in VPC Service ControlsDesigned to enhance AI security, the new capabilities we’re announcing today strengthen boundaries enforced by VPC-SC.

VPC Service Controls now support conditional access rules based on specific MCP attributes, including mcp.toolName , mcp.method , and mcp.tool.isReadOnly .

"At Mercado Libre, VPC Service Controls serve as an essential, foundational layer of our security architecture.

While identity and network controls effectively secure the front door, VPC Service Controls provide a criti…

This includes leveraging Google Cloud Confidential Computing with Intel TDX, NVIDIA Confidential Computing with NVIDIA Blackwell GPUs, our Titanium security architecture with the Titan chip, and a co-engineered open-source host stack to ensure verifiable transparency.

To dive deeper into this collaboration, read our blog post: Powering the next era of Confidential AI.

Advancing confidential foundationsGoogle Cloud is committed to making Confidential Computing capabilities broadly available across our infrastructure.

Accelerating secure collaboration: Confidential Space with H100 GPU supportTo power secure multi-party AI and machine learning, Confidential Space support for NVIDIA Hopper GPUs…

Developed in collaboration with Google Cloud, the studio will help British organisations move beyond AI experimentation to deploy autonomous, action-oriented AI systems at scale.

Deloitte is also committing to upskill 1,000 members of its UK AI and data workforce on Gemini Enterprise.

We also continue to foster the next generation of British unicorn startups through our ongoing partnership with Tech Nation at the London AI Hub.

At Google, we are committed to carbon-free energy 24/7, ensuring that the UK’s AI growth does not come at the cost of our climate goals.

Architecting the future togetherGoogle Cloud is the primary partner for the UK’s agentic transition.

SkillOpt treats an agent skill file as a trainable parameter outside a frozen target model, turning skill writing from one-shot prompting into a controlled optimization process.

SkillOpt keeps skills compact and auditable through bounded text edits, validation gating, rejected-edit feedback, and slow/meta updates, avoiding uncontrolled prompt drift.

The optimized skills transfer across model scales, agent harnesses, and related tasks, suggesting that they capture reusable workflow knowledge rather than benchmark-specific instructions.

Today, agent skills typically come from three sources: experts write them by hand, a frontier model generates them one-shot, or the agent loosely revises them…

Memora is a scalable memory system that dramatically increases agent productivity on long-horizon tasks by decoupling what is stored (rich memory content) from how it’s retrieved (lightweight abstractions and cue anchors), balancing abstraction and specificity.

is a scalable memory system that dramatically increases agent productivity on long-horizon tasks by decoupling is stored (rich memory content) from it’s retrieved (lightweight abstractions and cue anchors), balancing abstraction and specificity.

Why this is hard: the abstraction–specificity tensionExisting memory systems fall into two extremes.

None of these resolves the underlying tension between abstraction (which keeps memory effi…

As black-box models spread, the gap between prediction and understanding has become one of the central problems in computational neuroscience.

GCT distills brain-prediction models into short, readable accounts of what each patch of cortex responds to, then tests those claims.

An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up.

An LLM writes new stories engineered to activate a specific brain area, subjects hear them in the scanner, and if the explanation is correct, the targeted region lights up.

To build trust in the explanation, GCT uses an LLM to write new stories in w…

At a glance Talos is an open-source tool for automated, iterative reanalysis of genomic data in rare disease.

Deployed across a prospective cohort of almost 5,000 undiagnosed patients, Talos delivered 241 new diagnoses (5.1% additional yield).

On monthly iterative cycles, analysts only needed to review one new variant per 200 patients, demonstrating that frequent, systematic reanalysis can be run sustainably.

Why genome reanalysis mattersGenomic testing has transformed the diagnosis of rare disease, but even with this advancement, more than half of patients remain undiagnosed after their first test.

Looking aheadTalos reframes genomic reanalysis from a rare, labor-intensive event into a con…

At a glance Project Ire identifies a LOTUSLITE variant that shares TTPs (tools, tactics, procedures) with the public family but none of its indicators of compromise (IOC).

On Ire’s calibrationOne noteworthy observation in Ire’s report (opens in new tab) is worth highlighting first.

The Ire report does not surface a matching entry-point name, but it identifies that the behavioral shape is the same.

Ire never named LOTUSLITE in its report or chain of evidence.

Ire described the behavior precisely enough to make the mapping straightforward of this sample to LOTUSLITE.

At a glance Data Formulator 0.7 is an open-source AI-powered system for enterprise data analytics that combines data connectivity, agent-guided exploration, and visualization refinement in a shared workspace.

Enterprise teams increasingly rely on AI systems for analytics, but enterprise data workflows are often fragmented across storage systems and tools.

Listen now Opens in a new tabConnecting enterprise data with Data ConnectorsData Formulator helps teams bring enterprise data into an AI-ready workspace without needing to rebuild the same connections for every source of data.

Data Connectors provide persistent connections between enterprise data sources and Data Formulator, allowing analy…

At a glance Modern AI systems are powerful not because they replicate human intelligence, but because they presuppose it, by extending structures already present in human cognition and language.

Understanding AI as an extension of human intelligence—not a replacement for it—offers a more grounded path for building trustworthy AI systems.

Rather than asking whether AI systems are becoming intelligent in the human sense, these approaches ask a more basic question: What if AI systems work because they rely on structures that are rooted in human cognition?

In our recent paper, The Origins of Artificial Intelligence in Natural Intelligence, we argue that modern AI systems are best understood nei…

Built as the next generation of Magentic-UI, it combines a redesigned app with a harness optimized for small models.

MagenticBrain and Fara1.5 are small models designed for orchestration and computer-use tasks, respectively.

Together, these releases explore how far agentic performance can be pushed with smaller models, codesigned tools, and an optimized execution harness.

Today, Microsoft Research AI Frontiers releases MagenticLite (opens in new tab), an experimental agentic application designed for small models.

The result is an agent that runs efficiently, keeps data on the user’s machine, and supports a broad range of agentic tasks.

Vega puts these building blocks together into a single proof system.

The hashing problem, and how folding solves itA credential proof must do two expensive things: hash the credential bytes with SHA-256 and verify the issuer’s digital signature.

Making it zero-knowledge, cheaplyA proof system needs to be zero-knowledge: the verifier should learn nothing beyond the claim being proved.

Device bindingA zero-knowledge credential proof is only useful if it is tied to the person holding the credential.

The proof system powering Vega is already available as the open-source spartan2 (opens in new tab) project on GitHub.

Our recent paper, “LLMs Corrupt Your Documents When You Delegate”, has generated discussion about the reliability of AI systems in delegated workflows.

Using a controlled evaluation methodology, we examine how well information is preserved across these extended workflows.

We use chained transformation-and-inversion tasks that evaluate whether semantic content is preserved accurately across extended delegated workflows.

Azure AI Foundry Labs Get a glimpse of potential future directions for AI, with these experimental technologies from Microsoft Research.

At the same time, the findings should not be interpreted as evidence that AI systems lack practical value in real-world work today.

mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free.

The mimalloc memory allocator was initially designed in 2020 as a fast allocator for the state-of-the-art Lean (opens in new tab) and Koka (opens in new tab) programming languages developed at RiSE, both of which use novel compiler-guided reference counting (see Perceus).

ja .LBB0_generic leaq 7 ( %rsi ), %rax ; round to sizeof(void*) andq $-8 , %rax movq 232 ( %rdi , %rax ), %rcx ; rcx = heap->small_pages[index] movq 8 ( %rcx ), %rax ; block = rax = page->free testq %rax , %rax ; block == NULL?

Thus, mimalloc has three free lists per (64 KiB) mimalloc page, and effectively that …

Microsoft releases a lightweight foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings in grid analysis.

It provides a foundation for the community to build advanced power grid simulators and planning tools without recreating data or models from scratch.

Microsoft introduces GridSFM, a small foundation model for solving AC optimal power flow (AC-OPF) problems in transmission power grids.

Power grids face increasing strain from surging demand, the need to integrate renewable energy sources, transportation electrification, and extreme weather events.

This release adds the first open AC-OPF model that supports multiple grid topo…

Now we have experimentally synthesized it and measured its thermal conductivity (152 W/m/K) to be close to the thermal conductivity of silicon.

Now we have experimentally synthesized it and measured its thermal conductivity (152 W/m/K) to be close to the thermal conductivity of silicon.

Faster simulation : We have accelerated MatterSim-v1 model inference by 3-5x and integrated it with the LAMMPS software package, enabling large-scale simulations across multiple GPUs.

These include experimental validation of MatterSim predictions for thermal conductors, performance improvements for faster simulation, and the introduction of a new multi-task foundation model for materials characterization.

Le…

In our simulated multi-agent marketplace, agents accepted the first proposal they received up to 93% of the time without exploring alternatives.

Introducing SocialReasoning-BenchFigure 1: Our benchmark measures agents’ social reasoning ability in two domains, calendar coordination and marketplace negotiation.

Finding 3: Outcome optimality shows how much value agents leave on the table.

In calendar, agents perform better but still settle below the midpoint on average.

Finally, Outcome Optimality works well in settings with clear boundaries, where a “good” outcome can be defined and measured.

The ability to study transmission-level power grid behavior is essential for modern power systems research.

In most of the world, including the United States, realistic transmission-level grid data is classified as critical infrastructure information and subject to strict access controls.

These restrictions exist for good reasons, but the resulting lack of realistic grid models is increasingly exacerbating the challenges power systems face.

In this work, we introduce an open-data-derived pipeline for constructing large-scale, transmission-level power grid models that realistically approximate existing networks without relying on proprietary or restricted datasets.

Using only publicly access…

Inspired by MIT’s motto, “mens et manus” (mind and hand), she shared: “We really want students to be able to use physical AI.

The economic impact of MIT on this country is equivalent to the 14th largest GDP in the world.

She further highlighted MIT for America, an initiative expanding access to calculus, a required course for institutions such as MIT, in under-resourced high schools nationwide.

“What we [ASU] learn from MIT is, where’s the edge of technology,” said Crow.

Kornbluth expressed her hope for MIT to continue its longstanding tradition of research and education in service of the nation’s next 250 years.

A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon.

Q: What is agentic AI and how is it different from generative AI models like ChatGPT and Claude?

A: Agentic AI is AI that takes actions in the world.

Q: What are some promising applications of agentic AI?

Q: What does the future hold for agentic AI?

The MIT Music Technology and Computation (MTC) Graduate Program — launched in fall 2024 as a collaboration between the Music and Theater Arts Section in the School of Humanities, Arts, and Social Sciences (SHASS), and the School of Engineering (SoE) — presented its inaugural MIT Music Technology Research Showcase on May 13.

Each scholar presented inspiring exemplars of artful engineering that reflected the broader and burgeoning music technology scene at MIT.

“The MIT Music Technology and Computation Graduate Program taught me so much about the possibilities at the intersection of STEM and the arts," she says.

What does it mean to build music technology in this context?

All considered, the …

Q: What inspired “Beyond Data-Driven Aesthetics,” and what questions does it explore?

A: The conceptual origins of “Beyond Data-Driven Aesthetics” emerged from three intersecting lines of research.

Second, the exhibition was influenced by research in design computation and shape grammars that investigates relationships between human insight and computation through rule-based methods, rather than purely data-driven learning.

The exhibition itself is organized around five thematic areas: Aesthetic Measure, Aesthetic Guidelines, Algorithmic Aesthetics, Aesthetic Appropriation, and Aesthetic Novelty.

A: “Beyond Data-Driven Aesthetics” is conceived both as a research exhibition and as an ongoing…

David Autor, the Daniel (1972) and Gail Rubinfeld Professor in the MIT Department of Economics, has been named head of the Department of Economics, effective July 1.

“David is a world-class labor economist,” says Agustín Rayo, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences.

“I’ve been at MIT since 1999, and I owe my career to the Institute, the department, and colleagues who are as kind as they are accomplished,” Autor says.

Autor serves as co-director of the National Bureau of Economic Research (NBER) Labor Studies Program.

In 2024, Autor was one of five senior scholars selected by the Schmidt Sciences Foundation as an AI2050 Senior Fellow.

Their “Masked Inverse Reinforcement Learning” (Masked IRL) approach uses a large language model (LLM) to elaborate on ambiguous prompts based on the data collected from a user’s demo.

To learn new tasks in these situations, Masked IRL uses the robot’s sensors to capture information about its surroundings.

These masks gave Masked IRL a key advantage over comparable baselines in both 3D and real-world demos because it taught a robot which information to prioritize.

During simulation experiments, CSAIL researchers also found that Masked IRL was a fast learner.

Masked IRL senses and explains what users leave unsaid, but soon, it might “see” it too.

Over the past 80 years, America’s bold, sustained investment in scientific research, and the discoveries, ideas and innovations that flowed from it made America a world leader.

The nation’s scientific leadership has been essential to our shared prosperity and national security, and delivered real benefits for all Americans.

Likewise, Prof. John Urschel, a former NFL player, emphasizes the importance of collaboration and having a wide range of interests.

The benefits of scientific collaborationWhat happens when scientific disciplines join forces at MIT?

“Computers have changed everything, including science.”The state of American scienceWithin the profiles, interviewees were asked what needs …

To improve efficiency, researchers from MIT and Microsoft developed an intelligent system that streamlines the process of designing agentic workflows and automatically optimizes how those workflows are implemented.

It adjusts those configurations on the fly based on each user’s priorities, such as minimizing costs or maximizing speed.

“Agentic workflows are getting very complicated and quickly becoming the backbone of what cloud providers are doing.

They need to define which AI agents, models, and tools to use, and the order in which to use them.

When tested on diverse agentic workflows for video Q&A and code generation, Murakkab met user requirements while using only about 35 percent of th…

It was presented in collaboration with two of MIT’s strategic initiatives: the MIT Generative AI Impact Consortium (MGAIC) and the MIT Human Insight Collaborative (MITHIC).

“Paying attention to the societal consequences of AI is not a departure from MIT’s mission; it’s a way of ensuring that our technical leadership has maximum impact,” Rayo said.

He argued that AI will likely create new specialized work, requiring proactive policies around worker training, wage insurance, and broader capital ownership.

Her research team is now working on a new audit of the 2026 U.S. midterm elections, using a redesigned survey with input from political science experts.

“So there are absolutely examples of …

A new chip developed by MIT researchers could help tiny, low-power UAVs avoid obstacles as they zip around tight corners inside an industrial HVAC system to check for gas leaks.

The chip allows small autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments in real-time using only about as much power as a single LED.

This system-on-a-chip consumes only about 6 milliwatts of power, a fraction of the power required by other systems.

It required only about 2.5 percent of the power that the best existing chip for map construction would need.

Gleanmer makes that possible for the first time in a chip you can hold between your fingers,” says Karaman.

That’s because even the most powerful simulation techniques struggle to model the complex chemical arrangements in most of today’s solid materials.

Now a team of MIT researchers has created a way to accurately model the behavior of metals, regardless of the complexity of their chemical arrangement.

At the center of the approach are machine-learning models that make simulations of materials faster and more accurate.

The researchers improved those models by building training datasets that capture the diversity of atomic environments in chemically disordered materials.

“Chemical disorder means there’s a huge variety of local chemical environments, which is hard for the machine-learning model t…

On June 9, The Boston Globe released its 2026 “Tech Power Players” list, recognizing 50 influential local leaders in technology and business across Massachusetts.

Advancing AI and entrepreneurshipWhen it comes to AI, MIT is “working to drive artificial intelligence forward in sectors where the region is strongest, from biotechnology and robotics to defense and clean energy.

Looking ahead, The Globe highlights how MIT aims to remain a central driver of AI advancement within higher ed.

And when it comes to applying AI technologies to real-world problems, MIT aims to ensure the greater Boston area remains a leader.

The most promising area for the Greater Boston tech sceneThe Globe concludes by…

2026 BAIR Graduate ShowcaseCongratulations to the Berkeley Artificial Intelligence Research (BAIR) Lab class of 2026!

This year, BAIR celebrates another remarkable group of Ph.D. graduates whose curiosity, creativity, and perseverance have pushed the frontiers of artificial intelligence and machine learning.

Their work spans the breadth of modern AI — robotics and embodied intelligence, large language models and reasoning, computer vision, generative modeling, AI safety, human-AI interaction, AI for science and healthcare, and much more.

Along the way, they have published influential research, built systems with real-world impact, mentored their peers, and shaped the BAIR community for th…

Adaptive Parallel Reasoning: The Next Paradigm in Efficient Inference ScalingOverview of adaptive parallel reasoning.

We provide a detailed analysis of recent progress in the field of parallel reasoning, especially Adaptive Parallel Reasoning.

Figure 4: Special Tokens Variants across Adaptive Parallel Reasoning PapersInference Systems for Adaptive ParallelismHow do we actually execute parallel branches?

Figure 14: Difference in Model Choice Across Adaptive Parallel Reasoning PapersEach paper also offers a slightly different interpretation about how adaptive parallel reasoning contributes to the research field.

(Yang et al., 2025; Lian et al., 2025) aim to deliver sequential-AR-model-level a…

Large, learned world models are becoming increasingly capable.

Why is adversarial robustness an issue for world model planning?

We thus exploit the differentiability of learned world models $F_{\theta}$, while not falling victim to the inherent sensitivity of the state Jacobians $D_s F_{\theta}$.

It’s a funny sweet spot where the background literature (planning and control overall) is incredibly mature and well-developed, but the current setting (pure planning optimization over modern, large-scale world models) is still heavily underexplored.

But, once we figure out all the right ideas, world model planners will likely become as commonplace as RL.

Identifying Interactions at Scale for LLMsUnderstanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence.

Therefore, grounded or reality-checked interpretability methods must also be able to capture these influential interactions.

In this blog post, we describe the fundamental ideas behind SPEX and ProxySPEX, algorithms capable of identifying these critical interactions at scale.

SPEX and ProxySPEX FrameworkTo discover influential interactions with a tractable number of ablations, we have developed SPEX (Spectral Explainer).

We formalize this through two observations: sparsity (relatively f…

We developed a framework that enables direct evaluation and optimization of imaging systems based on their information content.

The first approach treated imaging systems as unconstrained communication channels, ignoring the physical limitations of lenses and sensors.

Our Information-Driven Encoder Analysis Learning (IDEAL) method uses gradient ascent on information estimates to optimize imaging system parameters.

The standard approach to computational imaging design, end-to-end optimization, jointly trains the imaging hardware and a neural network decoder.

The computational efficiency of IDEAL suggests possibilities for designing imaging systems that were previously intractable.

RL without TD learningIn this post, I’ll introduce a reinforcement learning (RL) algorithm based on an “alternative” paradigm: divide and conquer.

We can do Reinforcement Learning (RL) based on divide and conquer, instead of temporal difference (TD) learning.

There are two classes of algorithms in RL: on-policy RL and off-policy RL.

We compared TRL with $n$-step TD learning with different values of $n$, from $1$ (pure TD) to $\infty$ (pure MC).

I still think one of the most important problems in RL (and even in machine learning) is to find a scalable off-policy RL algorithm.

Previously, getting started on SageMaker Studio after discovering a model on Hugging Face required navigating multiple steps.

What’s newThis launch introduces three capabilities that shorten the path from a Hugging Face model to a working SageMaker Studio workflow.

Walkthrough: Deep-linking from Hugging Face to SageMaker StudioLet’s walk through the experience of customizing or deploying a model starting from Hugging Face.

By connecting Hugging Face directly to the SageMaker Studio workflows, developers can stay in their flow.

To get started, visit the Amazon SageMaker Studio page or explore models on Hugging Face and choose Deploy or Customize on SageMaker AI.

Amazon Nova Customizable Content Moderation Settings (CCMS) addresses this by letting approved customers selectively adjust safeguards across four responsible AI (RAI) pillars.

Amazon Nova enforces essential, non-configurable controls for responsible use of AI, such as controls to prevent harm to children and preserve privacy.

See Customize Amazon Nova in Amazon SageMaker AI using Direct Preference Optimization for a step-by-step walkthrough.

Importantly, these customizations operate within the bounds of Amazon Nova universal, non-configurable protections, ensuring that core safeguards remain intact regardless of adapter configuration.

These techniques power Amazon Nova CCMS, which provides…

MiniMax models on Amazon BedrockAmazon Bedrock supports three models from the MiniMax M2 family.

Amazon Bedrock continues to expand its catalog of MiniMax models as new versions become available.

Two endpoints for accessing MiniMax models on Amazon BedrockAmazon Bedrock provides two endpoints for invoking MiniMax models: bedrock-mantle and bedrock-runtime .

Getting started with MiniMax M2.5 in Amazon BedrockComplete the following steps to start using MiniMax M2.5 in Amazon Bedrock.

To control which identities can generate or use Amazon Bedrock API keys, see Control permissions for generating and using Amazon Bedrock API keys.

Amazon SageMaker AI also offers multi-turn RL as a fully managed, serverless capability, bringing this technique to SageMaker training jobs with no infrastructure to manage.

For these cases, the multi-turn RL infrastructure for Amazon Nova on Amazon SageMaker HyperPod gives you the compute, orchestration, and reward-routing layers to train agents on these complex workflows.

Amazon Nova delivers frontier intelligence and industry-leading price performance, and Amazon Nova Forge extends this with multi-turn RL training capabilities.

In this post, you deploy a two-phase infrastructure for multi-turn RL using Amazon Nova Forge on Amazon SageMaker HyperPod.

ConclusionYou now have a production-re…

When Nova identifies visual PII elements, it delegates the segmentation task to SAM 3 to produce exact boundaries for redaction.

Familiarity with Amazon Bedrock, Amazon SageMaker AI, Amazon Simple Storage Service (Amazon S3), AWS Lambda, AWS Step Functions, Amazon EventBridge, and Amazon Textract.

Based on Nova’s classification, the Step Functions workflow selectively invokes one or both specialized processes: the textual process if Nova detected textual PII, the visual process if Nova detected visual PII, or both processes in parallel if Nova identified both types.

Finally, Amazon Textract returns the coordinates of the text that Nova identified as PII for use in the next step.

To get star…

In this post, you learn how to use the new MLflow integration with Amazon SageMaker AI optimized inference recommendation jobs and Amazon SageMaker AI benchmark jobs to automatically stream experiment data into a unified tracking interface.

Solution overviewWith this release, when you submit an optimized inference recommendation job or a benchmarking job, Amazon SageMaker AI automatically streams results into a SageMaker MLflow app of your choice.

Set up Amazon SageMaker Studio for your development environment on Amazon SageMaker AI.

Create a SageMaker MLflow App from SageMaker Unified Studio (SageMaker Unified Studio → MLflow → Create MLflow App).

Step 3: Verify the MLflow App and define e…

This year’s accepted papers reveal a clear direction: open frontier models and open AI infrastructure have become foundational to how modern AI science gets done.

Approximately 2,000 accepted papers cite NVIDIA GPUs, and 145 cite NVIDIA Nemotron — a family of open models, including open datasets — as the foundation for new research.

Hundreds more draw on NVIDIA Cosmos, NVIDIA Isaac GR00T, BioNeMo and other NVIDIA open model families, spanning physical AI, robotics, autonomous vehicles and biomedical research.

AI for life sciences was fueled by NVIDIA BioNeMo open models and research contributions that help researchers understand protein function, molecular behavior and genetic code.

Sakana …

Why AI Capabilities MatterThe urgency for countries to build and deploy AI capabilities has grown with the rise of generative and agentic AI, which is reshaping markets, inspiring new industries and transforming existing ones — from gaming to healthcare.

Ingredients of a National AI StrategyThere are five ingredients of a national AI strategy:AI Imperative: Domestic AI capabilities are critical to economic growth, national security, cultural preservation and innovation — with responsible, trustworthy AI aligned to local policies as well as national goals.

AI-Ready Workforce: A wide spectrum of local AI skills and talent, plus basic AI literacy across the population.

National AI Strategies U…

Summer is heating up — and GeForce NOW is taking players along for the ride.

Start the month with Monopoly: Star Wars Heroes vs. Villains, bringing a galaxy far, far away to the iconic board-game franchise, alongside 12 new games joining the cloud this month.

Plus, don’t let the sun set on the biggest GeForce NOW savings of the year.

GeForce NOW makes it easy to take the battle between the light and dark sides across nearly any device.

Plus, check out this spreadsheet, made by a community member, featuring discounted games streaming on GeForce NOW and build out a bigger library at the best bargains during the Steam Summer Sale.

Emerging AI companies historically have had limited access to capital-intensive infrastructure, with even long-term commitments insufficient to unlock financing for compute.

Through this new model with NVIDIA, AI cloud companies will sell cloud services delivered through NVIDIA DSX AI factories that manufacture tokens at scale.

NVIDIA AI Factory Capacity Built Around DemandThe initiative is already taking shape, with AI cloud companies building DSX AI factories designed to serve customers and workloads across regions.

“This strategic collaboration with NVIDIA marks a pivotal moment in Sharon AI’s mission to deliver sovereign, large-scale AI compute infrastructure,” said James Manning, cofou…

America is a nation of builders.

For 250 years, America has built railroads that connected a continent, power grids that lit up cities, factories that powered prosperity, semiconductors that made the digital age possible and the internet that opened knowledge to the world.

Now, America is building again.

NVIDIA and its partners are investing in American manufacturing, supply chains, energy grids and skilled workforces so the U.S. can produce the infrastructure needed for better healthcare, breakthrough scientific discovery, stronger industrial productivity and global technology leadership.

It depends on the wide range of physical components behind them: advanced semiconductors, packaging, p…

In this post, we show how databases can use these technologies to accelerate GPU query execution.

Data layout and transfer orchestrationThe GQE data layer is optimized to efficiently transfer data from host memory to device memory.

CompressionGQE receives two main benefits from compression: query dataset capacity and query acceleration.

Apply GQE best practices to data platformsDatabase engines can translate NVIDIA Grace Blackwell hardware features into measurable query performance gains with targeted optimizations.

These optimizations reduce transfer time and compose into sophisticated query execution using NVIDIA cuDF, NVIDIA nvCOMP, and other CUDA-X libraries.

Claude Science integrates with NVIDIA BioNeMo Agent Toolkit as a resource that scientists can access within their workflow.

BioNeMo Agent Toolkit gives these agents the context needed to connect each step with an appropriate NVIDIA scientific capability.

Claude Science integrated with BioNeMo Agent Toolkit and NVIDIA NIM microservices accelerates high-throughput inhibitor prediction, optimization and validation.

NVIDIA BioNeMo Agent Toolkit gives scientific agents the accelerated tools they need to operate at the speed of science.

NVIDIA BioNeMo Agent Toolkit is open and harness-agnostic, allowing the same scientific skills to work across agent frameworks and research platforms.

Deep Infra uses the NVIDIA inference software stack to serve frontier open source models performantly on Blackwell from day zero, including DeepSeek V4.

The software stack determines whether that complexity turns into wasted capacity or lower cost per token.

NVIDIA’s inference software stack does this by connecting three layers:Production Operation: Coordinates distributed serving, orchestration, autoscaling and memory management so inference can run across the right compute and storage resources.

NVIDIA’s inference software stack is designed to make those layers work together so each optimization can build on the others.

Open Source Amplifies the Full-Stack AdvantageThat same full-stack fo…

As a robotics software engineer who leads the team behind NVIDIA Isaac ROS (Robot Operating System), Singh works on the connective tissue of the physical AI era.

Built on the open source ROS 2 framework, Isaac ROS brings CUDA-accelerated libraries and AI models to developers building autonomous mobile robots, manipulation systems and humanoids.

“Compared with the original Isaac SDK, Isaac ROS is completely modular,” Singh said.

With open software, developers can inspect the code, change it, contribute fixes and carry it forward.

“When more people can build robots,” Singh said, “the future gets here faster.”Follow @nvidialife on Instagram and learn more about NVIDIA life, culture and careers.

Vision AI agents are becoming a practical way to automatically turn video data from the physical world into operational intelligence in factories, cities, warehouses and transportation systems.

Turning that data into useful action requires vision AI agents that can understand video, adapt to real-world conditions and connect insights to operational workflows.

NVIDIA Metropolis agent skills and blueprints give developers reusable workflows to build, operate and optimize vision AI agents across that lifecycle.

Where Vision AI Agent Projects Can Get StuckAs organizations move toward autonomous vision agents, three challenges often come up:Accuracy Plateaus With Data Gaps: Vision AI agents need…

Anthropic’s Claude models in Microsoft Foundry — hosted on Microsoft Azure and running on NVIDIA GB300 Blackwell Ultra GPUs — are now generally available, giving Azure-native enterprises a powerful new way to build autonomous and domain-specific AI agents.

Enterprises can run Claude agents on Azure by using the NVIDIA Secure Agent Workspace Reference Design.

It provides a blueprint for running autonomous agents in a governed environment where identity, network access, credentials and runtime policy are controlled at the infrastructure level.

Claude in Microsoft Foundry accelerated by NVIDIA GB300 GPUs on Azure builds on the strategic partnership Microsoft, NVIDIA and Anthropic announced in …

This time, rather than sending massive volumes of raw data home for processing that takes weeks or months, Ocula will run AI algorithms directly on orbit, using Jetson to extract critical insights and transmit only the most relevant information — based on customer need — back to Earth in near real time, vastly reducing latency and costly downlink.

For Blue Ghost Mission 2, a lunar lander will separate and descend to the far side of the moon — carrying science and technology instruments including a radio telescope — in support of NASA-funded, UC Berkeley-led research to detect faint signals from the cosmic Dark Ages shortly after the Big Bang.

Meanwhile, Firefly’s Elytra spacecraft will cont…

Showcasing the importance of open source innovation in American AI, Palantir’s new intelligent engine — introduced today — uses NVIDIA Nemotron open models to serve the needs of U.S. government agencies.

Today’s Palantir announcement brings NVIDIA Nemotron open models into air-gapped environments — secure setups that are completely isolated from unsecured networks — on NVIDIA accelerated computing.

Palantir will use NVIDIA Nemotron open models to build custom frontier-quality models to serve the U.S. government.

While NVIDIA Nemotron open models provide a customizable and continually learning model layer on Palantir’s Sovereign AI Operating System, enterprise-grade deployments can be suppor…

This year marks Meta’s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it.

Some of the core maintainers of Python are Meta engineers who have authored new features and Python Enhancement Proposals (PEPs) for the Python community.

These improvements are vital for protecting the global Python community and ensuring that developers everywhere – including our own engineers – can safely share and consume packages.

These investments help grow the Python community and foster the new talent that is essential for Python…

Why Asset Classification MattersAsset classification is the foundation for many privacy controls.

The rest of this post walks through those pieces using asset classification as the case study.

All three share a single judge model, a larger reasoning model deliberately different from the classifier model.

Distill Stable Behavior Into RulesEven a strong LLM classifier should not be the default enforcement path forever.

Expand to other PAI workflows: The same pattern (context → LLM reasoning → distillation → deterministic enforcement) applies to lineage validation, purpose-boundary checking, and retention policy assignment.

The retrieval system within industry recommendation systems have consisted of microservices stitched together, with neural networks inconsistently integrated.

Under Index as Model previous microservice-based item indices used for retrieval become a tensor inside the model.

Moving From Microservice Mesh to One Integrated Neural NetworkThe Microservice Paradigm We ReplacedTraditional recommendation retrieval is built as a mesh of microservices.

We call this Index as Model: Every retrieval component — the item index, eligibility filter, scoring layer and user tower — becomes a tensor or operator inside a single PyTorch model.

Index FreshnessWith index as a model module, maintaining index fresh…

On its face the new Friend Bubbles feature looks simple enough.

It highlights Reels your friends have watched and reacted to.

On this episode of the Meta Tech Podcast, Pascal Hartig chats with Subasree and Joseph, two software engineers from the Facebook Reels team, about what it took to bring Friend Bubbles to life.

If you’ve ever underestimated a “simple” feature, this one’s for you.

And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.

We’ve fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that’s most relevant to them.

We’ve adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address the major friction points people experience when searching community content.

Addressing the Friction Points in Community KnowledgePeople struggle with three friction points when searching for answers in community content – discovery, consumption, and validation.

The Solution: A Modernized Hybrid Retrieval ArchitectureWe engineered a hybrid retrieval architecture that powers a discussions module on Facebook Search.

R…

We’ve built a unified AI agent platform that encodes the domain expertise of senior efficiency engineers into reusable, composable skills.

Introducing the Capacity Efficiency ProgramWhen the code you ship serves more than 3 billion people, even a 0.1% performance regression can translate to significant additional power consumption.

Many engineers at Meta use our efficiency tools to work on these problems every day.

Skills : These encode domain expertise about performance efficiency.

The pipeline mirrors the defensive AI Regression Solver:Gather context with tools: The AI agent looks up: Opportunity metadata.

Challenging the Conventional Wisdom on AI Context FilesRecent academic research found that AI-generated context files actually decreased agent success rates on well-known open-source Python repositories.

Our codebase is the opposite: proprietary config-as-code with tribal knowledge that exists nowhere in any model’s training data.

Any team with a large, proprietary codebase can benefit:Identify your tribal knowledge gaps.

What’s NextWe are expanding context coverage to additional pipelines across Meta’s data infrastructure and exploring tighter integration between context files and code generation workflows.

This approach turned undocumented tribal knowledge into structured, AI-readable con…

This is the second post in the Ranking Engineer Agent blog series exploring the autonomous AI capabilities accelerating Meta’s Ads Ranking innovation.

We introduce KernelEvolve, an agentic kernel authoring system used by Ranking Engineer Agent and generally applicable to a range of AI models beyond Ads Ranking.

Unlike typical large language model (LLM)-based agents that perform one-shot code generation, KernelEvolve treats kernel optimization as a search problem.

A standard coding assistant lacks the context to write optimized MTIA kernels because it has never seen MTIA documentation, instruction set details, or programming idioms.

KernelEvolve represents an early step toward the vision of …

To overcome this, we have developed the Meta Adaptive Ranking Model, which effectively bends the inference scaling curve with high ROI and industry-leading efficiency.

Introducing Meta Adaptive Ranking ModelServing LLM-scale & complexity models in a real-time ads recommendation environment requires resolving a fundamental tension between model complexity and system efficiency.

Adaptive Ranking Model addresses these challenges through a paradigm shift powered by three core innovations across the serving stack:Inference-efficient model scaling: Adaptive Ranking Model achieves a model complexity equivalent to the O(10 GFLOPs) per token used by top-tier LLMs.

To minimize compute overhead, Adapt…

Concurrent with the 2026 American Concrete Institute (ACI) Spring Convention, Meta is releasing a new AI model for designing concrete mixes – Bayesian Optimization for Concrete (BOxCrete), as well as the foundational data used to develop award-winning concrete mixes.

Amrize operates 18 cement plants, 141 cement terminals and 269 ready-mix concrete sites across North America.

Alongside the event, Meta is releasing a new AI model for designing concrete mixes, Bayesian Optimization for Concrete (BOxCrete).

How Meta Leverages AI for Concrete MixturesMeta’s AI for concrete model can help suppliers more quickly incorporate U.S. materials into their mixes through an approach called adaptive experi…

Friend bubbles in Facebook Reels highlight Reels your friends have liked or reacted to, helping you discover new content and making it easier to connect over shared interests.

Friend bubbles enhance the social experience on Facebook Reels by helping you discover content your friends enjoy, creating a shared viewing experience and sparking new conversations.

Along with additional optimizations in the underlying method, this approach enabled us to ship friend bubbles while preserving core Reels performance.

Friend bubbles work because the signal is high value: It adds meaningful social context that helps people decide what’s worth watching.

Engagement also scales consistently with the number …

Meta’s Ranking Engineer Agent (REA) autonomously executes key steps across the end-to-end machine learning (ML) lifecycle for ads ranking models.

Powering these interactions are highly sophisticated, complex and massively distributed machine learning (ML) models that continuously evolve to serve both advertisers and people who use the platforms.

Optimizing these ML models has traditionally been time-consuming.

To address this, Meta built the Ranking Engineer Agent, an autonomous AI agent designed to drive the end-to-end ML lifecycle and iteratively evolve Meta’s ads ranking models at scale.

ML training jobs run for hours or days, far beyond what any session-bound assistant can manage.

Nowhere is this more apparent than in mobile security, where a single class of vulnerability can be replicated across hundreds of call sites scattered throughout a sprawling, multi-app codebase serving billions of users.

Meta’s Product Security team has developed a two-pronged strategy to address this:Designing secure-by-default frameworks that wrap potentially unsafe Android OS APIs and make the secure path the easiest path for developers, andLeveraging generative AI to automate the migration of existing code to those frameworks at scale.

The result is a system that can propose, validate, and submit security patches across millions of lines of code with minimal friction for the engineers w…

RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend.

We want to iterate on collectives, transports, and novel features quickly on AMD platforms.

With RCCLX, we have integrated CTran to AMD platforms, enabling the AllToAllvDynamic – a GPU-resident collective.

These features provide significant performance improvements on AMD platforms and we are excited to share this with the community.

RCCLX Quick Start GuideInstall Torchcomms with RCCLX backend by following the installation instructions in the Torchcomms repo.

A Catching JiTTest focuses specifically on finding regressions introduced by a code change.

Agentic development dramatically increases the pace of code change, straining test development burden and scaling the cost of false positives and test maintenance to breaking point.

And since the JiTTest itself is LLM-generated, it can often infer the plausible intention of a code change and simulate possible faults that may result from it.

With them engineers no longer have to spend time writing, reviewing, and testing complex test code.

READ THE PAPERJust-in-Time Catching Test Generation at Meta

Piotr Niedźwiedź, CEO/CTO and founder of neptune.aiI’m excited to share that we’ve entered into a definitive agreement to be acquired by OpenAI, subject to closing conditions.

We are thrilled to join the OpenAI team and help their AI researchers build better models faster.

Neptune is a metrics dashboard company.”We’ve worked closely with OpenAI to create the metrics dashboard that helps teams building foundation models.

Our future with OpenAINeptune will join OpenAI and continue to support AI researchers with tools to monitor, debug, and evaluate frontier models.

We are looking forward to working with top AI researchers and supporting OpenAI’s mission of ensuring that AGI benefits all of hu…

For instance, financial data is highly sensitive and protected by very strict regulations, and synthetic data mimics the real data distribution without revealing customer information.

Read more about how leading foundation model teams curate their training data and other topics in the State of Foundation Model Training Report 2025.

Choosing the right synthetic data generation technique depends on the type of data and its complexity.

Synthetic tabular data generation is a promising direction to overcome these challenges by learning the distribution of the tabular data.

Post-processingAs the distribution of tabular data is highly complex, it makes the synthetic tabular data generation very ch…

TL;DR LLM embeddings are the numerical, vector representations of text that Large Language Models (LLMs) use to process information.

Unlike their predecessor word embeddings, LLM embeddings are context-aware and dynamically change to capture semantic and syntactic relationships based on the surrounding text.

What are the applications of LLM embeddings?

Word EmbeddingsSparse Word Embeddings One-Hot Vectors 1970s TF-IDF1980s Co-Occurrence MatrixStatic Word Embeddings Word2Vec 2013 GloVe 2014Contextualized word embeddings ELMo 2018 GPT-1 2018 BERT 2018 LLAMA 2023 DeepSeek-V1 2023 GPT-4 2023Static word embeddingsStatic word embeddings, such as word2vec in 2013, marked a significant development.…

TL;DR Dead neurons silently waste compute and reduce effective model capacity in foundation models.

Dead neurons’ impactRecent studies into dead neurons in the context of foundation models show interesting, albeit worrying, results.

These large reported fractions of dead neurons in foundation models are a concern from a computational perspective.

Before we move on to discuss how to detect and fix dead neurons, let’s touch upon an important distinction between dead neurons and vanishing gradients.

Further reading How to Monitor, Diagnose, and Solve Gradient Issues in Foundation Models Read moreVisualizing activation distributionsIs your foundation model suffering from dead neurons?

In the first part of this series, we covered the fundamentals of instruction fine-tuning (IFT).

def calculate_irs(instruction, output, reference_model): evaluation_prompt = f""" Instruction: {instruction} Model Output: {output} Rate how well the output follows the instruction on these criteria: 1.

| SourceHINT addresses a computational inefficiency in standard instruction fine-tuning: repeatedly reprocessing the same task instruction with every input example.

Read more about foundation model training infrastructure and other topics in Neptune’s 2025 State of Foundation Model Training Report.

First, during initial instruction fine-tuning across multiple diverse tasks, the model learns genera…

Large Language Model (LLM) inference at scale is challenging as it involves transferring massive amounts of model parameters and data and performing computations on large tensors.

In the following, we’ll use the Llama model family architecture as a specific example to understand the LLM workload at inference.

For a far more detailed analysis of the LLM workload at inference, see the chapter All About Transformer Inference in the book How to Scale Your Model, published by Google DeepMind.

See also How to Run LLMs Locally Read moreA quick primer on hardware for LLM inferenceA typical LLM inference cluster consists of several nodes, each with a multi-core CPU and multiple accelerator devices, …

In this article, we’ll explore the fundamental concepts of LLM grounding as well as strategies for optimally grounding models.

What is LLM grounding?

LLM grounding is analogous.

If relevant knowledge cannot be inferred from the data, then LLM grounding cannot yield more relevant responses.

When grounding LLMs using RAG, consider retaining only a few of the top hits (i.e., top-k) for your retrieval queries.

TL;DR Instruction fine-tuning (IFT) refines pre-trained large language models (LLMs) to follow specific task instructions by training on prompt-response pairs.

Instruction fine-tuning in a nutshellIFT tailors LLMs to follow user instructions by bridging their inherent next-word prediction with human-defined objectives.

Related LLM Fine-Tuning and Model Selection Using Neptune and Transformers Read moreParameter-efficient instruction fine-tuningWhile major foundation models like GPT-4 or Llama-2 undergo full parameter instruction fine-tuning during development, parameter-efficient fine-tuning (PEFT) methods have become widely adopted for instruction fine-tuning since the LoRA paper was publi…

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Paper: https://arxiv.org/abs/2511.08923 Abstract:

Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation …

Paper: https://arxiv.org/abs/2501.00663 Abstract:

Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling of dependencies, however, comes with a quadratic cost, limiting the model to a fixed-length context. We present a new neural long-term memory module that learns to memorize historical context and helps attention to attend to the current context while utilizing long past information. We sh…

https://arxiv.org/abs/2510.17558 Abstract:

We propose an extension of the decoder Transformer that conditions its generative process on random latent variables which are learned without supervision thanks to a variational procedure. Experimental evaluations show that allowing such a conditioning translates into substantial improvements on downstream tasks. Author: François Fleuret Links:

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Paper: https://arxiv.org/abs/2508.21038 Abstract:

Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realist…

Part of a series of monthly puzzles done in collaboration with MoMath.

Full video: https://youtu.be/l6DKRf-fAAM

Full video: https://youtu.be/l6DKRf-fAAM

What is the fundamental compressibility of language?

Check out our virtual career fair: https://3b1b.co/talent

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Manim scenes by Aaron Gostein and Grant Sanderson

Shannon’s story, as well as those for various pi creatures, by Mitchell Zemil.

Lunar robot and prediction/compression coin by Paul Dancstep

NanoGPT animations by Clayton Rabideau Shannon’s “A Mathematical Theory of Communication”

https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf Shannon’s “Prediction and Entropy of Printed English”

https://www.princeton.edu/~wbialek/rome/refs/shannon_51.pdf Scientific American article that…

Recent puzzle solutions on Patreon:

https://members.3blue1brown.com/posts/158885046?pr=true

Made as part of a monthly series of puzzles for the 2026 Year of Math.

On "The Print Gallery", by M.C. Escher

Full video: https://youtu.be/ldxFjLJ3rVY

Part of a series of monthly puzzlers. Stay subscribed to see the solution

Escher's Print Gallery, and the tour of complex analysis it invites.

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Home page: https://www.3blue1brown.com Original paper by de Smit and Lenstra:

https://pub.math.leidenuniv.nl/~smitbde/papers/2003-de_smit-lenstra-escher.pdf Timestamps: 0:00 - The print gallery

13:04 - Conformal maps from complex analysis

21:41 - The complex exponential

25:56 - The complex logarithm

32:32 - 3b1b Talent

33:14 - Constructing the key function

40:16 - The deeper math behind Escher ------------------ These animations are largely made us…

Part of a monthly series of puzzlers, in collaboration with MoMath and Peter Winkler

On the volumes of higher-dimensional spheres

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Home page: https://www.3blue1brown.com Thanks to UC Santa Cruz for letting me film there, and special thanks to Pedro Morales-Almazan for arranging everything. My video on Numberphile with a fun application of this problem: https://youtu.be/6_yU9eJ0NxA Timestamps:

0:00 - Introduction

1:01 - Random puzzle

6:16 - Outside the box

14:35 - Setting up the volume grid

21:14 - Why 4πr^2

25:21 - Archimedes in higher dimensions

36:17 - The general formul…

Part of a series of monthly puzzles, done in collaboration with MoMath.

https://momath.org/mindbenders

Solution to last month's probability puzzle.

Unexpected applications and a beautiful proof.

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Home page: https://www.3blue1brown.com Credits:

Senia Sheydvasser: Co-writing and sphere deformation animations

Paul Dancstep: Those lovely fluffy sphere animations Vince Rubinetti: Music Timestamps:

0:00 - To comb a hairy ball

1:24 - Applications

8:46 - The puzzle of one null point

12:12 - The proof outline

16:41 - Defining orientation

21:44 - Why inside-out is impossible

25:59 - 3b1b Talent

27:44 - Final food for thought ------------------ These animati…

This is the first in a set of monthly puzzles, curated by Peter Winkler. This one was originally suggested by Richard Stanley. You can sign up to hear his description of the answer at http://momath.org/mindbenders

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here:

https://arxiv.org/abs/2506.06494 Sources:

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https://www.youtube.com/watch?v=55F9dY2Y1zc 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers GLM 5.2: https://z.ai/blog/glm-5.2 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here:

https://arxiv.org/abs/2602.21548 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi #deepseek

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here:

https://recursivemas.github.io/

https://github.com/RecursiveMAS/RecursiveMAS 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi Thumbnail design: https://felicia.hu

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here:

https://www.anthropic.com/research/natural-language-autoencoders

https://transformer-circuits.pub/2026/nla/index.html 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Nemotron 3 Ultra paper is available here:

https://research.nvidia.com/labs/nemotron/Nemotron-3-Ultra/ Free Rendering course and source code:

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Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi Thumbnail design: https://f…

Check the pinned comment for the link to the full interview. Could AI agents eventually become the "Games Master" driving your gaming storylines? We explore the concept of AI assisting players or creating dynamic, non-scripted narratives. Discover how AI is currently being tested inside immersive game environments to change how we play. 🧠 Hashtags: #aiingames #gaming #ai #gamedev #futuretech

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here:

https://github.com/google-deepmind/alphaproof-nexus-results

https://arxiv.org/html/2605.22763v1 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Anthropic's Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/

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Check the pinned comment for the link to the full interview. We're discussing whether a "second order Nobel" prize is on the horizon for AI-driven science. With over 3 million researchers already using AlphaFold, the real-world impact is already historic. Hear what the experts think about what comes next for scientific discovery! 🔬

Thank you to Google for the invite! 🙏 I use Lambda GPU Cloud myself to rent NVIDIA GPUs for my projects - I’d really appreciate it if you checked them out! https://lambdalabs.com/papers Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/

Thumb…

Check the pinned comment for the link to the full interview. In this quick clip, we explore which legendary scientist ranks higher among the experts. It's a fun debate that leads into an even bigger discussion about AI's role in future scientific breakthroughs. You won't want to miss the full deep dive with Demis Hassabis! ⚡️

Full video: https://youtu.be/huAwz_BR8WM

#shorts

О росте мультимодального качества рассказал Роман Исаченко, руководитель группы анализа изображений в Яндекс R&D. #искусственныйинтеллект #нейросети #мультимодальность #омнимодель #машинноеобучение #datascience #яндекс #ai #ml #технологии

А почему — рассказывает Роман Исаченко, руководитель группы анализа изображений в Яндекс R&D. #искусственныйинтеллект #нейросети #мультимодальность #омнимодель #машинноеобучение #datascience #яндекс #ai #ml #технологии

Рассказывает Роман Исаченко, руководитель группы анализа изображений в Яндекс R&D. #искусственныйинтеллект #нейросети #мультимодальность #омнимодель #машинноеобучение #datascience #яндекс #ai #ml #технологии

Рассказывает Роман Исаченко, руководитель группы анализа изображений в Яндекс R&D. #искусственныйинтеллект #нейросети #мультимодальность #омнимодель #машинноеобучение #datascience #яндекс #ai #ml #технологии

Рассказывает Роман Исаченко, руководитель группы анализа изображений в Яндекс R&D. #искусственныйинтеллект #нейросети #мультимодальность #омнимодель #машинноеобучение #datascience #яндекс #ai #ml #технологии

На конференции Data Fest 2026 в Белграде независимый исследователь Александр Николич рассказал практическую историю создания аудиоязыковой модели Borealis с бюджетом, сопоставимым со стоимостью MacBook. Больше контента для разработчиков: https://t.me/+owyCvdge8WIyNTUy #DataFest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

At Data Fest 2026 in Belgrade, Andrei Panferov from the Institute of Science and Technology Austria introduced Quartet II, a novel method for NVFP4 pre-training that recovers SOTA accuracy. He outlined the core challenges of low-precision LLM training and presented CUDA kernels tuned for Blackwell GPUs, ready for integration into real training pipelines. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #datafest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

На Data Fest 2026 в Белграде Дмитрий Коршунов, Team Lead ML в Ecom, показал, как безопасно обновлять продуктовых AI-агентов с помощью системы автометрик. На примере агента Яндекс AI для турецкого рынка он объяснил, как фиксировать регрессии до прода, сравнивать версии и принимать решение о релизе, когда простой «Hello, Agent» уже позади. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

На Data Fest 2026 в Белграде Вячеслав Костров, ML-инженер в Яндексе, рассказал, как uplift-модели решают бизнес-задачи Лавки: от персональных скидок до показа продуктовых подборок. Он разобрал постановку uplift-задачи, подбор метрик и построение политик, а также практические приёмы с лагранжианом и uplift-деревьями для баланса ограничений. Всё это — на примере реальных внедрений и с разбором типичных ошибок. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #datafest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #Mu…

At Data Fest 2026 in Belgrade, Karina Romanova, Senior LLM Research Engineer, presented HGRPO — a hierarchical modification of GRPO for multi-turn dialogue agents. Applied to a booking agent in Yandex Alice, the method improved truthfulness by 8.0 percentage points and reduced dialogue length by 10.7%. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

На Data Fest 2026 в Белграде Дарья Виноградова, лид команды компьютерного зрения, представила два важных майлстоуна архивного поиска: новую архитектуру распознавания текста и выделение смысловых структур. Эти изменения делают поиск человечнее — теперь можно искать не слова среди текста, а человека среди людей. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #DataFest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

На Data Fest 2026 в Белграде Егор Коновалов, ML-инженер, разобрал хаки, которые находят LLM-агенты, когда генерируют GPU/TPU-код: от тривиального обхода numerical tolerance до изощрённых атак на timing-измерения и эксплуатации дыр в test harness. А ещё Егор показал, какие методы защиты реально работают, а какие создают ложное чувство безопасности. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #datafest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

At Data Fest 2026 in Belgrade, Andrey Filatov from KREA AI broke down the current state of real-time video generation: which architectures dominate, how they differ, and what challenges arise from compute limits and memory bottlenecks. He also covered production solutions like distillation and caching, and shared his outlook for the next 2–3 years: what will soon become possible and which bottlenecks the industry still overlooks. More content for developers: https://t.me/+owyCvdge8WIyNTUy #datafest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #Reinforceme…

Доклад из секции ML & Education конференции Data Fest 2026 в гостях у Яндекса «AI-генерация учебного контента и проверка открытых ответов студентов». Спикер — Денис Королёв, доцент, МИЭМ НИУ ВШЭ. Больше материалов для разработчиков: https://t.me/+owyCvdge8WIyNTUy #datafest #DataFest2026 #AI #ML #LLM #GenAI #MachineLearning #DataScience #MLOps #AIAgents #RAG #ComputerVision #AutonomousDriving #Yandex #Яндекс #TechTalk #Developers #ArtificialIntelligence #ReinforcementLearning #MultimodalAI

описание: 0:00:00 Начало

0:00:58 Mythos снова доступен

0:10:14 OpenAI и 5% акций

0:16:28 Корея и полтриллиона

0:23:11 Huawei Ascend в Корее

0:25:31 Пентагон внедряет ИИ

0:32:03 DeepSeek и AGI

0:39:15 Китайский суперкомпьютер

0:44:34 Замеряли GPT5.6

0:55:38 Жадный Palantir

1:02:04 Илон Маск и мозг

1:09:27 Британец, суд и ИИ

1:13:04 Папа - китайский коммунист ИИ-саммари: В этом выпуске мы обсуждаем последние новости в области технологий, экономики и бизнеса, включая обновления в области ИИ, развитие индустрии чипов в Южной Корее и стратегические инициативы крупных компаний. Узнайте, как эти события влияют на глобальный рынок и будущее технологий. В этом эпизоде обсуждаются перспективы использ…

описание: 0:00:00 Начало

0:00:40 Sakana Fugu

0:05:08 Трамп заблокировал GPT

0:08:17 Китайцы торгуют Claude

0:12:48 Китайцы лучше Mythos

0:19:57 Microsoft продает OpenAI

0:21:36 Борис Черный и лупы

0:31:50 OpenAI сделали чип

0:35:21 Закон об ИИ в России

0:40:37 Робоферма в Челябинске

0:47:07 Роботы заменили людей

0:52:28 OpenAI патчит опенсорс

0:57:27 Cursor сделал агентов

1:02:34 Маск делает игры

1:06:48 Роботы-попрошайки ИИ-саммари: В этом выпуске обсуждаются важные события в мире технологий, включая предстоящий DataFest в Новосибирске, выход нового продукта Fugu от Sakana, влияние администрации США на развитие моделей ИИ, а также проблемы доступа к токенам в Китае и дистилляцию моделей дл…

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https://www.patreon.com/primerlearning Helpful resources if you want to learn more about neural networks

https://www.youtube.com/@AndrejKarpathy

https://course.fast.ai/

https://www.youtube.com/@WelchLabsVideo

https://www.youtube.com/@3blue1brown Early papers. These probably aren't helpful for understanding the concepts in this video, but if you're interested in history.

The Perceptron – A perceiving and recognizing automaton: https://bpb-us-e2.wpmucdn.com/websites.umass.edu/dist/a/27637/files/2016/03/rosenblatt-1957.pdf

The Perceptron: A probabilistic model for information storage and organization in the brain: https://www.ling.upenn.edu/courses/cogs501/Rosenblatt1958.pdf A Logical…

Don Lincoln is a particle physicist at Fermilab who has spent decades working at the frontiers of high energy physics.

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Jean-Baptiste Kempf is lead developer of VLC and president of VideoLAN.

Kieran Kunhya is a longtime FFmpeg contributor, codec engineer, and the person behind the now-infamous FFmpeg account on X.

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Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(03:00) – Sponsors, Comments, and Reflections(10:48) – Weirdest things VLC opens(15:12) – How video playback works(24:33) – Video codecs and containers(35:20) – FFmpeg explained(56:20)…

Lars Brownworth is a historian, teacher, podcaster, and author specializing in Viking history, medieval Europe, and the Byzantine Empire.

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Go to https://perplexity.ai/OUTLINE:(00:00) – Introduction(01:03) – Sponsors, Comments, and Reflections(08:57) – The start of the Viking Age(18:50) – Viking military strategy, tactics & technology(32:33) – Ragnar Lothbrok(42:00) – The Grea…

Jensen Huang is the co-founder and CEO of NVIDIA, the world’s most valuable company and the engine powering the AI computing revolution.

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Go to https://quo.com/lexOUTLINE:(00:00) – Introduction(00:26) – Sponsors, Comments, and Reflections(06:34) – Extreme co-design and rack-scale engineering(09:20) – How Jensen runs NVIDIA(28:41) – AI scaling laws(43:41) – Biggest blockers to AI scaling laws(45:25) – Supply chain(47:20) – Memory(53:25) – Power…

Jeff Kaplan is a legendary Blizzard game designer of World of Warcraft and Overwatch, now preparing to launch a new game, The Legend of California, from his new studio Kintsugiyama – available to wishlist on Steam today, with alpha later in March.

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Rick Beato is a music educator, interviewer, producer, songwriter, and a true multi-instrument musician, playing guitar, bass, cello & piano.

His incredible YouTube channel celebrates great musicians & musical ideas, and helps millions of people fall in love with great music all over again.

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Peter Steinberger is the creator of OpenClaw, an open-source AI agent framework that’s the fastest-growing project in GitHub history.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(03:51) – Sponsors, Comments, and Reflections(15:29) – OpenClaw origin story(18:48) – Mind-blowing moment(28:15) – Why OpenClaw went viral(32:12) – Self-modifying AI agent(36:57)…

Nathan Lambert and Sebastian Raschka are machine learning researchers, engineers, and educators.

Sebastian Raschka is the author of Build a Large Language Model (From Scratch) and Build a Reasoning Model (From Scratch).

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(25:11) – ChatGPT vs Claude vs Gemini vs Grok: Who is winning?

(36:11) – Best AI for coding(43:02) – Open Source vs Closed Source LLMs(54:41) – Transformers: Evolution of LLMs since 2019(1:02:38) – AI Scaling Laws: Are they dead or still holding?

Paul Rosolie is a naturalist, explorer, author of a new book titled Junglekeeper, and is someone who has dedicated his life to protecting the Amazon rainforest.

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Joel David Hamkins is a mathematician and philosopher specializing in set theory, the foundations of mathematics, and the nature of infinity, and he’s the #1 highest-rated user on MathOverflow.

He is also the author of several books, including Proof and the Art of Mathematics and Lectures on the Philosophy of Mathematics.

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Irving Finkel is a scholar of ancient languages and a longtime curator at the British Museum, renowned for his expertise in Mesopotamian history and cuneiform writing.

He specializes in reading and interpreting cuneiform inscriptions, including tablets from Sumerian, Akkadian, Babylonian, and Assyrian contexts.

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Michael Levin is a biologist at Tufts University working on novel ways to understand and control complex pattern formation in biological systems.

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(2:42:41) – Mind uploading(3:01:22) – Alien intelligence(3:16:17) – Advice for young people(3:22:46) – Questions for AGI

David Kirtley is a nuclear fusion engineer and CEO of Helion Energy, a company working on building the world's first commercial fusion power plant by 2028.

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Dan Houser is co-founder of Rockstar Games and is a legendary creative mind behind Grand Theft Auto (GTA) and Red Dead Redemption series of video games.

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Go to https://drinkLMNT.com/lexOUTLINE:(00:00) – Introduction(01:29) – Sponsors, Comments, and Reflections(11:32) – Greatest films of all time(23:45) – Making video games(26:36) – GTA 3(29:55) – Open world video games(32:42) – Character creation(36:09) – Superintelligent AI in A Bette…

Because I do think there’s a role for AI, a huge role for AI.

BURGER: Right, right.

BURGER: Right, right.

So I think that’s also something quite important here that, you know, AI can help facilitate.

And I think that’s not just applying AI to solve solutions through optimization but also thinking about this in an integrated way.

JANSSEN: Yeah, yeah, exactly.

TEEVAN: Yeah, yeah, yeah.

I’m curious what you have found particularly surprising about how people and organizations are leveraging AI right now.

And so I do like to picture a future of work where humans are flourishing with AI and where humans still get to do meaningful work.

And I’m very curious about how we can take advantage of AI and do more without running ourselves into the ground because we’re not AI, right?

And the question we’re going to discuss is, are machines intelligent?

No, no, that’s right, that’s right.

I mean, in some sense, you could potentially have a super intelligent system, right, that’s far more intelligent than anything else on the planet.

BURGER: Right, right.

At the same time, I think, you know, transformers are not intelligent in the way that a three-year-old is, right?

Join Microsoft’s Doug Burger and guests as they dig into the fundamental truths about AI and how it will reshape the future.

Technical advances are moving at such a rapid pace that it can be challenging to define the tomorrow we’re working toward.

In The Shape of Things to Come, Microsoft research leader Doug Burger and experts from across disciplines tease out the thorniest AI issues facing technologists, policymakers, business decision-makers, and other stakeholders today.

It’s important to understand what the emerging shapes are and how we should respond.” – Doug Burger, Technical Fellow and Corporate Vice President, Microsoft ResearchAbout Doug BurgerDoug Burger is a research leader in …

This week, machine learning researchers around the world will be attending the annual Conference on Neural Information Processing Systems, or NeurIPS.

In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward.

So around that time when I started my PhD at Penn, I was working in machine learning theory and algorithmic economics.

How had you experienced a lack of community or network of women in machine learning before the founding of WiML?

So particularly when working on topics related to fairness, I’ve ended up focusing a bunch on stuff to do with marginalized groups as part of my responsible AI work.

Today, I’m excited to talk about the Paraphrase Project, an effort I co-led exploring how advances in AI tools for protein design might impact biosecurity.

These “patches,” akin to those in cybersecurity, have now been shared with organizations globally to strengthen biosecurity screening.

The project highlights that the same AI tools capable of incredible good can also be misused, requiring us to be vigilant, thoughtful, and creative so we continue to get the most benefit out of AI tools while working to ensure that we avoid costly misuses.

So things like, how similar is this to that template, wild-type protein structure that we used as our conditioning information?

But I feel like broadly…

What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct control over recommendations through natural language. Together they explore how conversational interfaces could transform platforms like YouTube, TikTok, and news feeds while preserving the strengths of modern recommendation algorithms.

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers recommendation transparency, black-box auditing, and the future of platform accountability.

Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They unpack a real-world forecasting project with Comfama in Colombia, including messy data realities, interpretability tradeoffs, and why "data science for good" starts with the people impacted.

Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness matters. They unpack key dimensions of responsible AI, including robustness to adversarial attacks, privacy, explainability, and fairness, and discuss how LLMs introduce new risks like hallucinations. The episode closes with a look at "agentic" recommender systems, where tools and memory shift recommendations from ranked lists to end-to-end task completion.

Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often matter more than differences between books. The episode also explores how to model reader preferences, why reviews often reveal more about the reviewer than the text, and how LLMs can aid computational literary research while still falling short of human editors in creative writing.

Ekaterina (Kat) Filadova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically help platforms by providing clearer preference signals, and the challenge of distinguishing coordinated behavior from bot activity is more complex than it appears. This episode explores the intersection of machine learning and game theory, examining what happens when your training data actively responds to your algorithm.

Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows specialized recommenders improve utility for niche users while raising questions about filter bubbles and data privacy.

In this episode, host Kyle Polich speaks with Roan Schellingerhout, a fourth-year PhD student at Maastricht University, about explainable multi-stakeholder recommender systems for job recruitment. Roan discusses his research on creating AI-powered job matching systems that balance the needs of multiple stakeholders—job seekers, recruiters, HR professionals, and companies. The conversation explores different types of explanations for job recommendations, including textual, bar chart, and graph-based formats, with findings showing that lay users strongly prefer simple textual explanations over more technical visualizations. Roan shares insights from his "healthy friction" study, which tested …

In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enterprise and Society. David shares insights from his research on how machine learning models can inadvertently create unfairness, particularly for minority and niche user groups, even without any malicious intent. We dive deep into his groundbreaking work on Principal Component Analysis (PCA) and collaborative filtering, examining why these fundamental techniques sometimes fail to serve all users equally. David introduces the concept of "power niche users" - highly active users with specialized in…

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the intersection of human curation and machine learning in content discovery, discussing the concept of "algatorial" curation—where algorithms and editorial expertise work together. Key topics include the cold start problem, why every metric is just a "proxy metric" for what users actually want, the challenge of filter bubbles, and the importance of balancing familiarity with discovery. Cory shares insights on why TikTok's algorithm works so well (clean data and massive interaction volume), the crucial …

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University, Santiago explains the mechanics of eye tracking technology—how it captures gaze data and processes it into fixations and saccades to reveal user browsing patterns. He introduces the groundbreaking RecGaze dataset, the first eye tracking dataset specifically designed for recommender systems research, which opens new possibilities for understanding how users interact with carousel interfaces like Netflix. Through collaboration between psychologists and AI researchers, Santiago's work demonstrate…

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply apply XGBoost to tabular data, recommender systems require sophisticated hybrid approaches that combine multiple techniques. Our guest, Boya Xu, an assistant professor of marketing at Virginia Tech, walks us through a cutting-edge method that integrates three key components: collaborative filtering for dimensionality reduction, embeddings to represent users and items in latent space, and bandit learning to balance exploration and exploitation when deploying new recommendations. Boya shares insigh…

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Technology. Florian is working on Monasterium.net, Europe's largest online collection of historical charters, containing millions of medieval and early modern documents from across the continent. The conversation delves into why traditional recommender systems fall short in the digital humanities space, where users range from expert historians and genealogists to art historians and linguists, each with unique research needs and information-seeking behaviors. Florian explains the technical challen…

In this month's episode of ICYMI, hear from Chip Huyen, Andrey Kurenkov, Frank Basso and Gilbert Eijkelenboom, discussing why moats are shifting toward physical systems and accumulated product intuition, how Astrocade built vibe coding before the term existed, what it's really like inside a deafeningly loud AI data center, why only 15% of people are technically self-aware and whether AGI requires anything like consciousness. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1006⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (00:00) The Cost of Bu…

Gilbert Eijkelenboom, bestselling author of People Skills for Analytical Thinkers and founder of the training firm MindSpeaking joins Jon Krohn to make the case that communication is a core data skill, not an optional extra. Gilbert shares the “And, But, Therefore” framework for turning dense analysis into a story stakeholders act on, the research suggesting only around 15% of people are genuinely self-aware (and how journaling, meditation, and exercise help close that gap), how childhood experiences install behavioral “algorithms” we carry into the workplace and why behavior change precedes attitude change, so doing small, uncomfortable things for 30 days can rewire how you see yourself. A…

Could an AI get good enough at AI research to build its own, more capable successor and kick off a compounding loop? That’s recursive self-improvement (RSI) and it surged into the conversation after Anthropic revealed that, as of May 2026, Claude wrote more than 80% of the code merged into its production codebase. In this Five-Minute Friday, Jon Krohn separates today’s AI-assisted coding from true RSI, walks through the accelerating evidence - METR’s shrinking task “time horizon,” Google DeepMind’s AlphaEvolve, Andrej Karpathy’s overnight training-tuner, weighs Jack Clark’s 60% bet that AI builds its own successor by 2028 against the compute, data and “marketing” skeptics. As ever, Jon land…

Frank Basso, VP of Infrastructure at Lightning AI, joins Jon Krohn for a rare ground-level tour of the one layer of the AI stack the show had never covered in over a thousand episodes: the physical data center. Frank explains how Lightning AI provisions its 35,000-plus GPUs through hyperscale co-location, why everything new is liquid-to-chip cooled, how GPUs talk to each other over ultra-fast east-west networks, and what it’s actually like to stand inside a 110-decibel AI data hall. He also debunks the most persistent myths about data-center water and electricity use, and makes the case for fuel cells, nuclear power, and 800-volt DC distribution as the path forward. Additional materials: ⁠⁠…

Anthropic’s Claude Fable 5 was the most capable AI model ever released to the public and it lasted just three days before the US government forced it offline. Jon Krohn unpacks both halves of the story: what makes Fable 5 special, and why it was pulled. Fable 5 and its locked-down sibling Mythos 5 are the same model separated only by safeguards, in a new “Mythos-class” tier above Opus. Jon covers its state-of-the-art benchmarks, premium $10/$50-per-million-token pricing, conservative safety classifiers, and the federal export-control directive, reportedly sparked by an Amazon-flagged “jailbreak” that took it down. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/100…

For this episode #1001 special, the tables are turned: SuperDataScience founder Kirill Eremenko takes the host’s chair and Jon Krohn is the guest. They trace Jon Krohn’s path from an Oxford neuroscience PhD to a New York hedge fund to founding the AI consulting firm Y Carrot, why he regrets leaving academia and how tools like Claude Code erased his hard-won technical moat and why that makes skilled engineers more valuable than ever. Along the way: whether AI is a bubble, Jevons paradox and the data-center boom, the RICE framework for choosing AI projects, the single biggest reason AI projects fail and how a well-built AI agent could give anyone “Christopher Nolan–like” focus. Additional mat…

For this landmark 1,000th episode and the show’s 10-year anniversary, host Jon Krohn is joined by SuperDataScience founder Kirill Eremenko, who hosted the podcast for its first 400-plus episodes before handing over the reins. In a first for the show, the episode was recorded live with the audience invited to join on air, alongside surprise appearances from the team, longtime guests, and even Jon’s family. Together, Jon Krohn and Kirill look back on a decade of the podcast and field listener questions on AI’s biggest opportunities, the build-versus-buy dilemma, how to break into the field today, and how to stay grounded amid the relentless pace of AI. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠…

Chip Huyen joins host Jon Krohn for this milestone episode 999 to talk about her record-breaking book "AI Engineering" the most-read title on the O'Reilly platform last year and how the AI landscape has shifted since her last appearance. Chip breaks down what separates AI engineering from machine learning engineering, makes the case for a "start simple" workflow, gets candid about the real costs of running LLMs in production, and shares why she's now fascinated by physical AI, robotics, and world models and why the durable problems worth solving are increasingly human ones. Jon Krohn guides the conversation from the practical content of the book through to where the field is heading next. A…

In this month’s episode of ICYMI, Jon Krohn explores how AI agents are simultaneously creating new risks and unlocking powerful new ways of working with data. Hear from Anneka Gupta, Cal Al-Dhubaib, Trevor Manz, Jazmia Henry, Jeremy Mumford, and Jacob Miller, discussing why the old cybersecurity playbook breaks down in the age of Claude Mythos, how the notebook became an AI agent’s working memory, what it really takes to build a foundation model from scratch, and why failing slowly is the most expensive mistake an AI team can make. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/998⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email natali…

Dr. Andrey Kurenkov returns to the show to talk about Astrocade's astronomical growth from pre-alpha to over 20 million engaged users, what it actually takes to build a vibe-coding platform that scales, and how the broader AI landscape has shifted since his last appearance. Andrey shares behind-the-scenes lessons from building B2C user-generated content products, why the real moat is community rather than tech, and his current thinking on humanoid robotics, AGI, and the AI risks people actually overlook. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.superdatascience.com/997⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode…

TrueFoundry co-founder and CEO Nikunj Bajaj speaks to Jon Krohn about how enterprises like Nvidia and Siemens are realizing returns of over $100 million from single agent deployments, the AI gateway architecture that makes it possible to connect, observe, and govern agents at scale, and why the familiar advice to “start small” is the wrong way to roll out AI agents inside a large organization. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/996 Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.⁠⁠⁠ In this episode you will learn: (01:21) What TrueFoundry does and why agents in production nee…

Jazmia Henry joins Jon Krohn to break down what it actually takes to build end-to-end foundation models for the energy industry. From wrangling decades of handwritten oil-and-gas documents into usable training data, to bespoke tokenizers, reinforcement learning, and inference at scale, Jazmia walks through every stage of the stack. Along the way she explains why reinforcement learning models are "bursty," what reward hacking is and how her Grounded Continuous Evaluation framework fixes it, and revisits the 2023 NeurIPS paper that argued, to widespread skepticism at the time, that scaling bad data degrades model performance. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠h…

Unemployment for recent computer-science graduates now rivals rates for fine-arts and anthropology majors, and undergraduate CS enrollment fell 11% in 2025. In this Five-Minute Friday, Jon Krohn walks through the data on both sides of the debate, from Stanford research showing a 13% employment drop for young workers in AI-exposed jobs, to Federal Reserve studies finding no statistically detectable link between AI adoption and reduced hiring. Jon shares his own view on where the truth lies and offers five concrete pieces of advice for graduates and senior professionals alike on how to get hired in 2026. Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/993⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠…

For years, AI content has come in the form of “use this library, use this tool” tutorials that age out within months. Jacob Miller and Jeremy Mumford, co-authors of the brand new Wiley book Architected Intelligence, wanted to write something different, a guide to the higher-level principles of building AI products and AI-first organizations that will still be relevant in five or ten years. In this episode, the two Pattern engineers walk Jon Krohn through the core ideas of their book: why you should design products and processes so they can be executed by a human, an AI agent, or any hybrid combination; why most companies are still treating hallucinations as a model problem when they’re actu…

Global data center investment now surpasses global oil supply spending.

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This sort of operation is directly exploiting a core feature of internet social media platforms.

The main purpose of recommender systems is to recommend people the same items similar people show an interest in.

Some of the most common methods to implement recommender systems, use concepts such as cosine/correlation similarity, matrix factorization, neural autoencoders and sequence predictors.

As you say, recommender systems exist because the business model of social media platforms is to monetise attention.

F: So you are saying that this is not an accident: is this the basis of the optimisation of the recommender system?

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What is the state of AI and videogames?

This and much more is covered in this 1st episode of AI and videogames.

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Can NPCs in videogames leverage new LLM-based tech?

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Tech sovereignty takes 3 years and political will.

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Apple just spent $2B on tech that reads your silent speech.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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This episode breaks down programmable money, the technology that turns your wallet into a permission system.

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Data Science at Home explores the latest in AI, data science, and machine learning.

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The title of today’s episode is Bias in the machineC: Francesco, today we are starting with an infuriating discussion.

The failure of the medical community as a whole to recognise this obvious bias up to the 21st century is an example of how insidious the problem of bias is.

Three: The bias in your training sample: people put training samples together, and people have culture, experience, and prejudice.

These assumptions inform the way AI systems work—and fail—to this day.

When an algorithm is a black box and you can’t look inside, you have no way of analysing its bias.

Join the discussion on our Discord serverAfter reinforcement learning agents doing great at playing Atari video games, Alpha Go, doing financial trading, dealing with language modeling, let me tell you the real story here.In this episode I want to shine some light on reinforcement learning (RL) and the limitations that every practitioner should consider before taking certain directions.

RL seems to work so well!

What is wrong with it?

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Join the discussion on our Discord serverIn this episode I explain how a research group from the University of Lubeck dominated the curse of dimensionality for the generation of large medical images with GANs.

The problem is not as trivial as it seems.

Many researchers have failed in generating large images with GANs before.

One interesting application of such approach is in medicine for the generation of CT and X-ray images.Enjoy the show!

ReferencesMulti-scale GANs for Memory-efficient Generation of High Resolution Medical Images https://arxiv.org/abs/1907.01376

Join the discussion on our Discord serverTraining neural networks faster usually involves the usage of powerful GPUs.

In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.

Enjoy the show!

ReferencesFaster Neural Network Training with Data Echoinghttps://arxiv.org/abs/1907.05550