AI Digest.

OpenAI Raises $110B as Claude Code Ships Auto-Memory and Anthropic Open-Sources Skills Library

Multi-agent orchestration dominated the day as Karpathy shared honest results from running parallel AI researchers on ML experiments, revealing that agents can implement ideas but can't generate good ones. Career anxiety spiked with reports of YC startups cutting all engineers below staff level. Obsidian emerged as the community's preferred knowledge vault for agent-managed workflows.

Daily Wrap-Up

The AI discourse today split cleanly into two camps: people building increasingly ambitious agent systems and people wondering what those systems mean for their careers. @karpathy's detailed thread about running eight parallel AI researchers on nanochat experiments was the day's standout contribution, not because it worked (it didn't), but because it offered an honest, granular look at where multi-agent coordination actually breaks down. The agents can implement ideas but can't generate good ones. They run nonsensical experiment variations and fail to control for basic variables like training time. This is the kind of honest signal that cuts through the hype cycle, and it's especially valuable coming from someone with Karpathy's credibility in the space.

On the career anxiety side, @jeffdfeng's claim that YC founders are planning to lay off all engineers below staff/principal level landed hard. Whether the specific claim holds up or not, the pattern is directionally real: the floor of what constitutes a valuable individual contributor is rising fast. @zackbshapiro's thread about replacing specialized legal AI tools with general-purpose Claude tells a parallel story from professional services. Domain-specific SaaS is getting squeezed from below by increasingly capable foundation models that non-technical users can customize themselves. Meanwhile OpenAI announced a staggering $110B funding round, which means the infrastructure buildout behind all of this is just getting started.

The most entertaining moment was @cryptopunk7213 highlighting someone who deployed an AI agent to lowball sellers on Facebook Marketplace, netting a Jeep Wrangler for $1500 and three TVs for free. It's the kind of scrappy, slightly unhinged application that makes you realize agents are going to show up in places nobody planned for. The most practical takeaway for developers: if you're experimenting with multi-agent setups, invest your time in designing better experiment protocols and evaluation criteria for your agents rather than just scaling up the number of parallel workers. Karpathy's experience shows that the bottleneck is agent judgment quality, not parallelism.

Quick Hits

  • @sama announced OpenAI raised $110B from Amazon, NVIDIA, and SoftBank. The scale of capital flowing into AI infrastructure continues to be jaw-dropping.
  • @theo flagged that the government is attempting to force Anthropic to remove Claude's safety guards, calling it "probably very bad."
  • @UnslothAI updated Qwen3.5 with improved tool-calling and coding performance, running the 35B-A3B variant on just 22GB RAM.
  • @pvncher launched RepoPrompt 2.0 with a fully integrated agent, MCP tools, and context builder.
  • @aiedge_ posted a comparison of Perplexity Computer vs OpenClaw with "10 Mega Prompts."
  • @affaanmustafa shared how easy it is to add Claude Code integrations as a Cowork plugin.
  • @jackfriks captured the vibe of the moment: "claude, read this article and implement all of its advice" followed by "retires."
  • @nicdunz offered a philosophical take: prompting LLMs is similar to using the search bar on the Library of Babel website.
  • @Zephyr_hg posted about skills that will be worth $500/hour in 2027 that are free to learn today.
  • @TheBronxViking retweeted @BillyM2k's "how to run a company in 2026" meme.

Agents & Multi-Agent Orchestration

The conversation around agent orchestration shifted from theoretical to practical today, with several posts exploring what it actually looks like to run multiple AI agents on real tasks. The dominant theme was an emerging spectrum of agent complexity: tab completion, single agents, parallel agents, agent teams, and whatever comes next. We're collectively figuring out where on that spectrum current models actually deliver value versus where they just create expensive chaos.

@karpathy shared what might be the most detailed public account of multi-agent ML research, running four Claude and four Codex instances simultaneously on nanochat experiments:

> "The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at. [...] The agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully through experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly."

His framing of the problem is particularly sharp: you're now "programming an organization" where the source code is prompts, skills, tools, and processes. A daily standup becomes part of the org's codebase. He noted that agents are excellent at implementing well-scoped ideas but terrible at creatively generating them, which maps onto a broader pattern: current models are execution engines, not strategy engines. In a separate post, @karpathy shared Cursor's data showing the ratio of tab-complete to agent requests shifting over time, noting the balance between leverage and chaos:

> "If you're too conservative, you're leaving leverage on the table. If you're too aggressive, you're net creating more chaos than doing useful work."

This calibration problem resonated across several other posts. @nummanali highlighted Middleman, a tool built on the idea that developers have become project managers who need a middle management layer between themselves and their agents. @alxfazio pushed the opposite direction, advocating for "headless claude maxxing" with fully autonomous operation. And @Jaytel declared they're done with Claude Code entirely, finding that building a custom harness is "addicting." @trq212 shared Anthropic's own lessons from building Claude Code under the title "Seeing like an Agent," which frames the challenge from the tool-builder's perspective.

The convergence point across all of these is that the tooling layer between humans and agents is where the real innovation is happening right now. The models themselves are increasingly commodity; the orchestration, evaluation, and feedback loops around them are the differentiator. @cryptopunk7213's example of an agent autonomously lowballing Facebook Marketplace sellers is a perfect microcosm: the model capability was already there, the value was in the creative application and orchestration.

AI & Career Disruption

The career impact conversation reached a new intensity today, with multiple posts painting a picture of accelerating displacement across both technical and professional services roles. What's notable is that the anxiety isn't coming from outsiders speculating anymore. It's coming from founders, practitioners, and people actively making hiring decisions.

@jeffdfeng reported direct conversations with YC founders:

> "Spoke with several YC founders planning to lay off all engineers below staff/principal — basically everyone under L5. This only became viable after Opus 4.5 in December. The Block layoffs are a signal: the floor just collapsed."

Whether these specific claims are exaggerated or not, the direction is consistent with what @cgtwts shared from Anthropic's CEO about AI wiping out 50% of certain professional roles within 12 months. @alancarroII added dark humor with a meme about tradespeople watching AI replace everyone who went to college. The practical counterpoint came from practitioners who are already adapting. @zackbshapiro detailed how he's replaced specialized legal tech tools (Harvey, CoCounsel, Spellbook) with general-purpose Claude customized for his practice. @garthwatson echoed the pattern from the other side:

> "As a non-practising lawyer that just used Claude Code to build a mobile app, and having founded and scaled a legal tech company [...] this is signal."

The synthesis here is nuanced. The threat isn't that AI replaces people wholesale. It's that AI raises the minimum bar for what constitutes a valuable contributor. A lawyer who can build their own app with Claude Code is dramatically more dangerous in the market than one who can't. An engineer who can orchestrate multiple agents is worth more than one who can only write code manually. The floor is rising, and the people who are most at risk are those in the middle: skilled enough to have been valuable in the old paradigm, but not adaptable enough to leverage the new tools.

Local AI & Knowledge Management

Obsidian had a strong showing today as the community's preferred substrate for agent-managed knowledge systems. The appeal is straightforward: markdown files, local storage, plugin extensibility, and now apparently whatever feature gap was holding it back from agent integration has been closed.

@cameron_pfiffer declared the competition essentially over:

> "This is basically the only thing that was preventing Obsidian from being the go-to for agent-managed knowledge vaults. It's so over for Notion."

@noahvnct followed up with a guide on building an "AI Second Brain" using Obsidian and Claude Code, which represents a practical implementation of the knowledge vault concept. The broader implication was articulated by @matteopelleg, who argued that Apple will ultimately win the AI race by acquiring Anthropic and putting models that run on 32GB of RAM into every device, with perfect memory and access to all local files at zero marginal cost.

The thread connecting these posts is the growing conviction that the future of personal AI is local-first. Cloud-based AI services have the capability advantage today, but the privacy, latency, and cost advantages of local inference are becoming increasingly compelling as models shrink. For developers building agent systems, the choice of knowledge backend matters more than it might seem. Markdown-based systems like Obsidian offer the transparency and version control that agent workflows need, while proprietary formats create friction for both humans and agents trying to read and write programmatically.

Sources

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Unsloth AI @UnslothAI ·
Qwen3.5 is now updated with improved tool-calling & coding performance! Run Qwen3.5-35B-A3B on 22GB RAM. See improvements via Claude Code, Codex. We also benchmarked GGUFs & removed MXFP4 layers from 3 quants. GGUFs: https://t.co/4lSce5zZbO Analysis: https://t.co/rHZK8JWdYM
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Sam Altman @sama ·
We have raised a $110 billion round of funding from Amazon, NVIDIA, and SoftBank. We are grateful for the support from our partners, and have a lot of work to do to bring you the tools you deserve.
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Addy Osmani @addyosmani ·
Every abstraction shift in software history made devs more productive by raising the level of intent. This is the next step: from writing code to orchestrating systems that write code (building "the factory" for your code). The unsolved problem isn't generation but verification. That's where engineering judgment becomes your highest-leverage skill. To truly scale, think "factory model" - orchestrate fleets of agents like a production line: clear specs as blueprints, TDD for quality control, strong architecture to amplify leverage.
M Michael Truell @mntruell

The third era of AI software development

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Aakash Gupta @aakashgupta ·
The headline says AI intensifies work. What the study actually found is more interesting than that. Berkeley researchers tracked 200 employees for 8 months. AI made every single one of them more capable. They wrote code they couldn’t write before. They took on tasks they used to outsource. They moved faster on work that would have sat in a backlog for months. And then they burned out. Because the company changed nothing else. The org handed people a tool that 10x’d their ability to start new work, then kept the org chart, meeting cadence, review processes, and scope boundaries completely identical. Zero workflow redesign. This is like giving everyone a car and keeping the speed limit signs from the horse-and-buggy era. People drove faster because they could, crashed because nobody updated the roads. The self-reinforcing cycle the researchers found is worth sitting with: AI accelerated tasks → raised speed expectations → workers leaned harder on AI → scope expanded → wider scope created more work → more work demanded more AI. That loop has no natural stopping point. The company never installed one. Meanwhile, a separate NBER study across thousands of workplaces found productivity gains of just 3%. And an Upwork survey found 77% of employees say AI tools actually decreased their productivity. The pattern across all of this research is identical: individual capability goes up, organizational design stays frozen, and the gap between the two creates burnout. The study literally recommends companies build an “AI practice” with structured reflection intervals and scope limits. The researchers aren’t saying AI failed. They’re saying management failed to adapt to AI. Every CEO reading this headline as validation for slowing AI adoption is making exactly the wrong bet. The companies that win will be the ones that redesign the operating system around the intensity, not the ones that avoid it.
R Rohan Paul @rohanpaul_ai

Powerful new Harvard Business Review study. "AI does not reduce work. It intensifies it. " A 8-month field study at a US tech company with about 200 employees found that AI use did not shrink work, it intensified it, and made employees busier. Task expansion happened because AI filled in gaps in knowledge, so people started doing work that used to belong to other roles or would have been outsourced or deferred. That shift created extra coordination and review work for specialists, including fixing AI-assisted drafts and coaching colleagues whose work was only partly correct or complete. Boundaries blurred because starting became as easy as writing a prompt, so work slipped into lunch, meetings, and the minutes right before stepping away. Multitasking rose because people ran multiple AI threads at once and kept checking outputs, which increased attention switching and mental load. Over time, this faster rhythm raised expectations for speed through what became visible and normal, even without explicit pressure from managers.

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CG @cgtwts ·
Anthropic CEO: “AI will wipe out 50% of lawyers, consultants, and finance professionals within the next 12 months” https://t.co/fkuBs6VfhD
C Claude @claudeai

We've also created plugins across HR, design, engineering, ops, financial analysis, investment banking, equity research, private equity, and wealth management to help users see what's possible and start building their own.

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Thariq @trq212 ·
Lessons from Building Claude Code: Seeing like an Agent
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Thariq @trq212 ·
We've rolled out a new auto-memory feature. Claude now remembers what it learns across sessions — your project context, debugging patterns, preferred approaches — and recalls it later without you having to write anything down. https://t.co/c7PyGaukNQ
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tobi lutke @tobi ·
Pi is the most interesting agent harness. Tiny core, able to write plugins for itself as you use it. It RLs itself into the agent you want. I was missing cc’s tasks system and told it to spawn clause in tmux and interrogate it about it and make an implementation for itself. It nailed it, including the UX. Clawdbot is based on it and now it makes sense why it feels so magical. Dawn of the age of malleable software.
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Ihtesham Ali @ihtesham2005 ·
🚨 Anthropic just open-sourced the exact Skills library their own engineers use internally. Stop building Claude workflows from scratch. These are plug-and-play components that work across Claude Code, API, SDK, and VS Code copy once, deploy everywhere. What's inside: → Excel + PowerPoint generation out of the box → File handling and document workflows → MCP-ready subagent building blocks → Pre-built patterns for multi-step automation → Production templates you'd normally spend weeks writing The old way: re-explain your workflow every single chat. The new way: build a Skill once, Claude never forgets how you work. 100% Open Source. Official Anthropic release. Repo: https://t.co/XNx3i4yNy6
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Sudo su @sudoingX ·
this is what a 24gb VRAM builds in 2026. one prompt. ten files. 3,483 lines of code. zero handholding. i gave Qwen3.5-35B-A3B a single detailed spec describing the full game architecture and hit enter. enemy types, particle systems, procedural audio, powerups, boss fights, ship upgrades, parallax backgrounds, everything in one message. the model planned the file structure itself, wrote every module in dependency order, wired all the imports, and served the game on port 3001. it ran on first load. when it hit a bug in collision detection it read its own error output, found the issue, fixed it, and kept building. this is pure agent loop running on local hardware. what you're looking at is pixelated octopus aliens with tentacle animations, 4 layer parallax space background with planets at different depths, a full particle system handling explosions and ink splatter and engine trails and bullet impacts, procedural audio through Web Audio API with zero sound files loaded, unleash mode with combo multiplier, boss fights every 5 levels, ship upgrades that unlock as you progress. no libraries. no frameworks. vanilla JS and Canvas. 3B active parameters. single RTX 3090. llama.cpp with q8_0 KV cache at 262K context. Claude Code pointed at localhost:8080 through the native Anthropic endpoint. no API costs. 112 tok/s. a GPU you can buy used for $800. game is called Octopus Invaders and i actually like playing it.
S Sudo su @sudoingX

testing Qwen3.5-35B-A3B latest optimized version by UnslothAI on a single RTX 3090. one detailed prompt. zero handholding. watch a 3B model scaffold an entire multifile game project autonomously. the setup: > model: Qwen3.5-35B-A3B (80B total, only 3B active per token) > quant: UD-Q4_K_XL by Unsloth (MXFP4 layers removed in latest update) > speed: 112 tok/s generation, ~130 tok/s prefill > context: 262K tokens > flags: -ngl 99 -c 262144 -np 1 --cache-type-k q8_0 --cache-type-v q8_0 > engine: llama.cpp > agent: Claude Code talk to localhost:8080 (llama.cpp now has native Anthropic API endpoint. no LiteLLM needed) q8_0 KV cache cuts VRAM usage in half vs f16 at 262K. -np 1 is default but worth noting. parallel slots multiply KV cache and at 262K that's an instant OOM. the prompt was more detailed than this but you get the idea: build a space shooter with parallax backgrounds, particle systems, procedural audio, 4 enemy types, boss fights, power-up system, and ship upgrades. 8 JavaScript modules. no libraries. game's called Octopus Invaders. gameplay footage dropping next.

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Alan Carroll @alancarroII ·
Plumbers and electricians seeing AI replace everyone who went to college https://t.co/CgvnlfVlO7
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Jeff @jeffdfeng ·
Spoke with several YC founders planning to lay off all engineers below staff/principal — basically everyone under L5. This only became viable after Opus 4.5 in December. The Block layoffs are a signal: the floor just collapsed. If you’re early in your career, the next few years are everything. Your edge will be how well you integrate AI into the value you create. The fastest learners are about to compound at absurd rates.
J jack @jack

we're making @blocks smaller today. here's my note to the company. #### today we're making one of the hardest decisions in the history of our company: we're reducing our organization by nearly half, from over 10,000 people to just under 6,000. that means over 4,000 of you are being asked to leave or entering into consultation. i'll be straight about what's happening, why, and what it means for everyone. first off, if you're one of the people affected, you'll receive your salary for 20 weeks + 1 week per year of tenure, equity vested through the end of may, 6 months of health care, your corporate devices, and $5,000 to put toward whatever you need to help you in this transition (if you’re outside the U.S. you’ll receive similar support but exact details are going to vary based on local requirements). i want you to know that before anything else. everyone will be notified today, whether you're being asked to leave, entering consultation, or asked to stay. we're not making this decision because we're in trouble. our business is strong. gross profit continues to grow, we continue to serve more and more customers, and profitability is improving. but something has changed. we're already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that's accelerating rapidly. i had two options: cut gradually over months or years as this shift plays out, or be honest about where we are and act on it now. i chose the latter. repeated rounds of cuts are destructive to morale, to focus, and to the trust that customers and shareholders place in our ability to lead. i'd rather take a hard, clear action now and build from a position we believe in than manage a slow reduction of people toward the same outcome. a smaller company also gives us the space to grow our business the right way, on our own terms, instead of constantly reacting to market pressures. a decision at this scale carries risk. but so does standing still. we've done a full review to determine the roles and people we require to reliably grow the business from here, and we've pressure-tested those decisions from multiple angles. i accept that we may have gotten some of them wrong, and we've built in flexibility to account for that, and do the right thing for our customers. we're not going to just disappear people from slack and email and pretend they were never here. communication channels will stay open through thursday evening (pacific) so everyone can say goodbye properly, and share whatever you wish. i'll also be hosting a live video session to thank everyone at 3:35pm pacific. i know doing it this way might feel awkward. i'd rather it feel awkward and human than efficient and cold. to those of you leaving…i’m grateful for you, and i’m sorry to put you through this. you built what this company is today. that's a fact that i'll honor forever. this decision is not a reflection of what you contributed. you will be a great contributor to any organization going forward. to those staying…i made this decision, and i'll own it. what i'm asking of you is to build with me. we're going to build this company with intelligence at the core of everything we do. how we work, how we create, how we serve our customers. our customers will feel this shift too, and we're going to help them navigate it: towards a future where they can build their own features directly, composed of our capabilities and served through our interfaces. that's what i'm focused on now. expect a note from me tomorrow. jack

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cogsec @affaanmustafa ·
If you're a cowork user - its super duper easy to add as a plugin! I use a bit of everything at this point mainly to check how things work across harnesses but coworks plugin interface is super duper easy! get started in 30 seconds! cmd -> affaan-m/everything-claude-code https://t.co/D2yCymO53G
C cogsec @affaanmustafa

The Codex App is still heavily slept on if you aren't using ECC for Codex you're missing out Its super easy and pulls all the skills over Most peoples development related openclaw automations can also just be directly ran from codex I ported a lot of my automations over https://t.co/oCZRV3cvKb

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Shanaka Anslem Perera ⚡ @shanaka86 ·
Anthropic just announced it will take the Trump administration to court over the supply chain risk designation. And in the same breath, Axios revealed the detail that changes everything about this story. While Anthropic was being blacklisted for refusing to allow mass surveillance, the Pentagon’s own “compromise deal” that Under Secretary Emil Michael was offering on the phone at the exact moment Hegseth posted the designation on X would have required Anthropic to allow the collection and analysis of Americans’ geolocation data, web browsing history, and personal financial information purchased from data brokers. Read that again. The Pentagon spent two weeks saying it has no interest in mass surveillance of Americans. Then the deal they actually put on the table asked for access to your location, your browsing history, and your financial records. They told us Anthropic was lying. The contract language told us Anthropic was right. Now here is where this becomes an existential question for a $380 billion company. The supply chain risk designation means every company that does business with the Pentagon must certify they do not use Claude. Eight of the ten largest companies in America use Claude. Defense contractors, cloud providers, consulting firms, banks. The blast radius is not the $200 million Pentagon contract. It is the enterprise ecosystem that generates $14 billion in annual revenue. Anthropic’s legal argument is specific: under 10 USC 3252, the designation can only restrict use of Claude on Pentagon contract work. Your commercial API access, your https://t.co/koW5OJjjaM subscription, your enterprise license are, in Anthropic’s reading, completely unaffected. But here is the problem. That is a legal argument. It will take years to resolve in court. And in the meantime, every general counsel at every Fortune 500 company with any Pentagon exposure is going to ask one question: is using Claude worth the risk? The IPO, which was expected this year at a $380 billion valuation backed by $30 billion in fresh capital, is functionally frozen. No underwriter will price an offering while a company carries the same designation as Huawei. And here is the final detail nobody has processed yet. Hours after blacklisting Anthropic, the Pentagon accepted OpenAI’s proposed safety framework, which contains the identical red lines: no mass surveillance, no autonomous lethal weapons. They destroyed one company for a position they then accepted from its competitor. Full analysis on Substack. https://t.co/AEv8EMPdsZ
S Secretary of War Pete Hegseth @SecWar

This week, Anthropic delivered a master class in arrogance and betrayal as well as a textbook case of how not to do business with the United States Government or the Pentagon. Our position has never wavered and will never waver: the Department of War must have full, unrestricted access to Anthropic’s models for every LAWFUL purpose in defense of the Republic. Instead, @AnthropicAI and its CEO @DarioAmodei, have chosen duplicity. Cloaked in the sanctimonious rhetoric of “effective altruism,” they have attempted to strong-arm the United States military into submission - a cowardly act of corporate virtue-signaling that places Silicon Valley ideology above American lives. The Terms of Service of Anthropic’s defective altruism will never outweigh the safety, the readiness, or the lives of American troops on the battlefield. Their true objective is unmistakable: to seize veto power over the operational decisions of the United States military. That is unacceptable. As President Trump stated on Truth Social, the Commander-in-Chief and the American people alone will determine the destiny of our armed forces, not unelected tech executives. Anthropic’s stance is fundamentally incompatible with American principles. Their relationship with the United States Armed Forces and the Federal Government has therefore been permanently altered. In conjunction with the President's directive for the Federal Government to cease all use of Anthropic's technology, I am directing the Department of War to designate Anthropic a Supply-Chain Risk to National Security. Effective immediately, no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic. Anthropic will continue to provide the Department of War its services for a period of no more than six months to allow for a seamless transition to a better and more patriotic service. America’s warfighters will never be held hostage by the ideological whims of Big Tech. This decision is final.

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Andrej Karpathy @karpathy ·
I had the same thought so I've been playing with it in nanochat. E.g. here's 8 agents (4 claude, 4 codex), with 1 GPU each running nanochat experiments (trying to delete logit softcap without regression). The TLDR is that it doesn't work and it's a mess... but it's still very pretty to look at :) I tried a few setups: 8 independent solo researchers, 1 chief scientist giving work to 8 junior researchers, etc. Each research program is a git branch, each scientist forks it into a feature branch, git worktrees for isolation, simple files for comms, skip Docker/VMs for simplicity atm (I find that instructions are enough to prevent interference). Research org runs in tmux window grids of interactive sessions (like Teams) so that it's pretty to look at, see their individual work, and "take over" if needed, i.e. no -p. But ok the reason it doesn't work so far is that the agents' ideas are just pretty bad out of the box, even at highest intelligence. They don't think carefully though experiment design, they run a bit non-sensical variations, they don't create strong baselines and ablate things properly, they don't carefully control for runtime or flops. (just as an example, an agent yesterday "discovered" that increasing the hidden size of the network improves the validation loss, which is a totally spurious result given that a bigger network will have a lower validation loss in the infinite data regime, but then it also trains for a lot longer, it's not clear why I had to come in to point that out). They are very good at implementing any given well-scoped and described idea but they don't creatively generate them. But the goal is that you are now programming an organization (e.g. a "research org") and its individual agents, so the "source code" is the collection of prompts, skills, tools, etc. and processes that make it up. E.g. a daily standup in the morning is now part of the "org code". And optimizing nanochat pretraining is just one of the many tasks (almost like an eval). Then - given an arbitrary task, how quickly does your research org generate progress on it?
T Thomas Wolf @Thom_Wolf

How come the NanoGPT speedrun challenge is not fully AI automated research by now?

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Jaytel @Jaytel ·
I'm done with Claude Code— building your own harness in Pi is addicting
T tobi lutke @tobi

Pi is the most interesting agent harness. Tiny core, able to write plugins for itself as you use it. It RLs itself into the agent you want. I was missing cc’s tasks system and told it to spawn clause in tmux and interrogate it about it and make an implementation for itself. It nailed it, including the UX. Clawdbot is based on it and now it makes sense why it feels so magical. Dawn of the age of malleable software.

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Sam Altman @sama ·
Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.
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Aniket @Aniket_Singh04 ·
Nobody’s talking about what just happened to Anthropic: Anthropic built the AI that half the US government quietly depends on daily They were deep in a $200M Pentagon deal — one of the biggest AI contracts ever Anthropic drew two hard lines: Claude won’t surveil American citizens, Claude won’t pull a trigger without a human deciding The Pentagon said those lines needed to go. Anthropic said they weren’t moving (respect 🫡) Trump signed an order cutting Claude from every federal agency overnight The Pentagon then slapped them with a “national security risk” designation — the same one they gave Huawei Every classified system running Claude has 6 months to rip it out completely Sam Altman — Anthropic’s biggest competitor — publicly said OpenAI has the same rules and wouldn’t have budged either The US government just punished a company for refusing to let AI kill or spy unsupervised.
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Aidan Gold @MrGoldBro ·
Let me get this straight: Anthropic refused to work with DoW unless they could promise their tech wasn't used for surveillance or killing. DoW said that they need full capabilities. Anthropic declined to give full access. OpenAI stood by Anthropic for ensuring AI safety. Trump then cancelled all Anthropic usage across the government, including a $200m contract. OpenAI then submits a bid to replace Anthropic.
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Garth Watson @garthwatson ·
As a non-practising lawyer that just used Claude Code to build a mobile app, and having founded and scaled a legal tech company, and been heavily involved in the legaltech scene, I just wanna say this is signal.
Z Zack Shapiro @zackbshapiro

The Claude-Native Law Firm

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Mark Gadala-Maria @markgadala ·
Just a few hours ago he was on TV saying he stood by Anthropic. Then he undercuts them and takes the same contract that Anthropic just lost. How can anyone trust this guy?
S Sam Altman @sama

Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.

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Will Washburn @willwashburn ·
Introducing Agent Relay
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Ted Lieu @tedlieu ·
The Department of Defense just agreed to the same two conditions with OpenAI that Anthropic was asking for. Can someone explain? I genuinely don’t understand.
S Sam Altman @sama

Tonight, we reached an agreement with the Department of War to deploy our models in their classified network. In all of our interactions, the DoW displayed a deep respect for safety and a desire to partner to achieve the best possible outcome. AI safety and wide distribution of benefits are the core of our mission. Two of our most important safety principles are prohibitions on domestic mass surveillance and human responsibility for the use of force, including for autonomous weapon systems. The DoW agrees with these principles, reflects them in law and policy, and we put them into our agreement. We also will build technical safeguards to ensure our models behave as they should, which the DoW also wanted. We will deploy FDEs to help with our models and to ensure their safety, we will deploy on cloud networks only. We are asking the DoW to offer these same terms to all AI companies, which in our opinion we think everyone should be willing to accept. We have expressed our strong desire to see things de-escalate away from legal and governmental actions and towards reasonable agreements. We remain committed to serve all of humanity as best we can. The world is a complicated, messy, and sometimes dangerous place.

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Anthropic @AnthropicAI ·
A statement on the comments from Secretary of War Pete Hegseth. https://t.co/Gg7Zb09IMR
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Boris Cherny @bcherny ·
In the next version of Claude Code.. We're introducing two new Skills: /simplify and /batch. I have been using both daily, and am excited to share them with everyone. Combined, these kills automate much of the work it used to take to (1) shepherd a pull request to production and (2) perform straightforward, parallelizable code migrations.