LEOVLM

[LEOVLM @MCharles10581]

@ethanmckanna I don't think he cover that part while 'balancing'

[LEOVLM @MCharles10581]

@ethanmckanna sensor fusion algorithm and costs, hardware complexity, training data, and reliance on HD maps? These are major challenges when scaling to millions of vehicles and scale the entire autonomous driving service as efficiently as Tesla potentially could

[LEOVLM @MCharles10581]

@ethanmckanna don't have to encourage anything since I'm doing control engineering worked on autonomous driving projects, including sensor fusion and its trade-offs, I believe this 'tech youtuber' failed to mention the actual reason why waymo performs a detour and a "less aggressive" strategy

[LEOVLM @MCharles10581]

@ethanmckanna @linguinelabs @MKBHD good is he balancing both sides in that video?

[LEOVLM @MCharles10581]

@ethanmckanna @linguinelabs @MKBHD How to tell me you never watched his waymo video without telling me

[Mustafa Suleyman @mustafasuleyman]

We now have state-of-the-art pre-trained base models in the 3T-parameter weight class, built entirely in-house at MAI.

Post-training results coming soon -- stay tuned!

Lots of details in our technical blog post.

https://t.co/xcZ5uV4TAp

[LEOVLM @MCharles10581]

@zoidz00 @samsheffer

[LEOVLM @MCharles10581]

Now It's time for us to compare Tesla Roadster Launch Event to OpenAI dev day

[LEOVLM @MCharles10581]

@DobackHuffInc @TSLAFanMtl Of course a Canadian flag

[Nick @nickcammarata]

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had

with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works

https://t.co/zHij5zwKGT

[elvis @omarsar0]

Banger paper from Google DeepMind on co-evolving an agent's harness and its weights.

(bookmark it)

If you already log your harness changes and results, those logs can become training data for the model running inside the harness.

This is a nice paper from Google showing how to do this co-evolution.

Specifically, they propose Evo2, a framework for weight-harness co-evolution.

How it works:

One policy plays two roles. As a Proposer, it writes edits to its own harness, and as a Reflector, it diagnoses what each edit changed. Those edits and their outcomes then become training data for both roles, using SFT followed by interleaved GRPO.

The result:

Evo2 reaches 48.3% on SWE-bench Verified and 82.1% on Polyglot, against 41.0% and 62.6% for the strongest baselines. The harness part alone moves a GPT-5-mini agent from 24.4% to 48.3% on SWE-bench Verified.

The evolved harnesses also transfer to other benchmarks without further evolution.

Paper: https://t.co/ea6xgqY4HY

Chat with Paper: https://t.co/ueh8i82GcV

[levent @__alpoge__]

“Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial IoT devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off - though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested.

Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evals, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after.” -@bucketofkets @willzhou97

https://t.co/vuYMTgHdRG

[Anthropic @AnthropicAI]

New on our Engineering blog: Creating in-house source control.

In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.

Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: https://t.co/wzNkwHs7IN

[Xiangyi Li @xdotli]

Introducing Robo Use: API to physical worlds for agents

One interface, 32 embodiments, 100+ scenarios, and 11 agents and models

We aggregated all academic simulator scenarios and ran 2500+ trials with initial results.

Preview them or try it yourself 🧵

[Octi Zhang @octi_zhang]

(1/6) From @GoogleDeepmind’s AlphaGo to AlphaZero, we saw machines find moves that reached beyond what we’d imagined for them. No one taught them those strategies. They emerged from reinforcement learning: playing, failing, and trying again under a single binary task reward for success or failure. Inspired by them, we couldn’t help but wonder: what might a robot discover if we gave it that same freedom to learn?

That question led us to AlphaNIST. Starting tabula rasa, with that same binary reward plus only a simple control cost, a single policy solves all 20 @NIST assembly tasks at a 98.2% success rate in simulation. More surprising still, AlphaNIST can complete 10 different tasks in a row, receiving that reward only once, after all 10 are done.

Just as AlphaGo’s strategies surprised its creators, AlphaNIST discovered dexterous strategies we couldn’t have come up with ourselves.

Paper: https://t.co/HZBYgG7SWK
Website: https://t.co/VyUKiYFI2y

[Fish Audio @FishAudio]

$52M seed round. 15M+ users. But this is what we're most proud of yet.

Introducing Drama 3: the most emotive and controllable TTS model in the world

And yes, it's open-weight.

You can now control how even a single word should sound, change its tone, and shift your voice mid-sentence.

Uncle Roger was impressed too.

Try it here:

https://t.co/4nbz4sQ4yy

Comment “DRAMA” within the next 48 hours for 1 month free.

If you're an enterprise your first month is on us:

https://t.co/jBXXZSAg0p

[LEOVLM @MCharles10581]

@geniusvczh 《顺利》

[Antonio Lupetti @antoniolupetti]

"Advanced Linear Algebra with Applications" is a set of lecture notes that explores the mathematical foundations of numerical linear algebra and its applications to scientific computing, graph theory, and machine learning.

The notes cover matrix norms and decompositions, floating-point arithmetic, numerical errors and conditioning, LU and Cholesky factorizations, iterative methods such as Jacobi, Gauss–Seidel, and SOR, sparse matrices, graph Laplacians, Krylov subspace methods, conjugate gradients, Lanczos and Arnoldi algorithms, GMRES, and preconditioning techniques.

One particularly interesting section explains the mathematics behind Google's original PageRank algorithm. PageRank essentially treats the web as a directed graph and the ranking of its pages as an eigenvalue problem. Since the web graph is far too large for conventional matrix decompositions, iterative numerical methods make it possible to compute the rankings efficiently. This is an interesting example of how classical linear algebra became the foundation of one of the most influential technologies in the history of the web.

https://t.co/AHUSFtAIHs

[Math Cafe @Riazi_Cafe_en]

McGill's "Mathematical Tools for Computer Science"

📽️ Lecture Videos: https://t.co/YZsdU01rAS
📷 Course Website: https://t.co/sBXajCkYnS

[Tatsunori Hashimoto @tatsu_hashimoto]

Deep learning is often taught as a sanitized collection of facts and math. But work at the frontier of AI is messy and empirical.

Our new class, Deep Learning Alchemy, aims to teach the art and science of deep learning in the hands-on spirit of Language Modeling From Scratch.

Students build intuition by running experiments on small LMs, then test it through prediction quizzes: given an unseen code diff, can you predict what happens?

https://t.co/0H82T1yyam

My co-instructor @kothasuhas and I think there's significant demand for classes that treat students as serious learners who want to be assessed for what they’ve learned, and so we're trying a new exam-heavy approach to the class (7 exams, 85% of the grade, with the remaining 15% in-person discussion assessments).

We’ve designed the class so students can use AI agents to run their experiments and do their assignment, but they then have to demonstrate that mastery on exams without access to the assignment, and any partial credit on the exam has to come through citations to the assignment, incentivizing them to know what’s in their write-up.

The same assignments and quizzes are public for anyone to try (lecture recordings will be released through @StanfordOnline with some delay). We have some very fun assignments and quizzes covering both classic topics (like NQM, run-to-run stochasticity, and scaling laws) and new and timely topics like muP or hyperball. Made by some real experts like @TristanThrush and @wen_kaiyue

Try some quizzes below!