Research & Building | 2L @SyracuseU Law

NY
Joined April 2018
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Sean Latt retweeted
Replying to @jack
The only moat is “for the love of the game”
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Sean Latt retweeted
Replying to @themanbearpigV2
The AGI is the datacenter lease lawyers
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i've written this before but the era of open models is not gonna be bc of open frontier intelligence being super good vs the actual frontier, but because of open unrefused intelligence i dont care if it's frontier if it won't do what i'm asking it to do
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Sean Latt retweeted
So we put a hoop outside the Garden.
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"You will be civilized on the day you can spend a long period doing nothing, learning nothing, and improving nothing, without feeling the slightest amount of guilt." - Nassim Nicholas Taleb
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“I’ll b aight tho” 😭😭😭
We made the first AI model you can have a realistic conversation with that was trained entirely on speech! Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs. This is a GPT3 moment for speech.
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Sean Latt retweeted
need an intern who's into a few of the following: trading, pokemon, poker, sports/sports betting, perps, cs/ds, money, money, fine dining
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Sean Latt retweeted
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications. On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments. While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software. After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely. openai.com/index/securing-ou…
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Heard of a top law firm completely cutting off Harvey and all API calls to Claude/OpenAI after they saw what openAI did with navier strokes Now hosting everything locally with GPUs they own, running Kimi and deepseek Think you’ll see a lot more of this going forward
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Marc Andreessen says the gap between what AI-native startups and incumbents think AI can do is the widest he’s ever seen: "The AI-native early stage startups have just a completely different view on what they can do with AI than the typical incumbents in almost any industry, almost without exception." "AI is just racing ahead in capability even week by week... There are going to be a lot of industries that are really up for grabs in a fundamentally more profound way than maybe has been the case in the past." "The founders are gonna have a level of superpowers coming from the use of these tools and these technologies that they just simply haven't had in the past... Had I had Astra and Netscape in 1994, like, oh my God. Like, Ben, what would you and I have been able to do at Netscape?" "Those entrepreneurs are gonna color way outside the lines. We already see this. They're not gonna think of themselves as like, 'I'm just a software company,' or, 'I'm just a hardware company,' or even, 'I'm just an AI company.'" @pmarca @bhorowitz
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Sean Latt retweeted
Meet AutoRouter, the most intelligent way to pick models You’re wasting tokens selecting frontier models for every prompt, and sometimes for worse performance AutoRouter handles the decision layer: - Understands the prompt request - Picks the best model fit for the task - Saves you money at every turn - Plugs into your current setup Works with your plans & every leading model inside Codex/Claude Code Beta is live: autorouter.com
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Introducing the free Government & Legal Index for ChatGPT & Claude. Give your AI agents 30M+ US government pages, from federal statutes and court opinions to city codes and permits. Research tax rules, permits & case law with the Firecrawl plugin. firecrawl.dev/alexandria/gov…
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Legal next ?
We just added 41M+ life science papers to the Firecrawl Research Index. Your AI agents can now search drug discovery, clinical trial, and biology literature with 90% recall@10. All sourced from authoritative sources and 100% free. Live in the API via /search/research.
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Introducing the free Government & Legal Index for ChatGPT & Claude. Give your AI agents 30M+ US government pages, from federal statutes and court opinions to city codes and permits. Research tax rules, permits & case law with the Firecrawl plugin. firecrawl.dev/alexandria/gov…
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Last month, we demonstrated high-rate alchemy: turning mercury into real, stable gold, beating CERN’s record by a factor of 10. Last week, we enriched isotopes in the world’s most powerful plasma centrifuge, beating the Soviet record that stood for 40 years. Now, we’re building a fusion reactor in San Francisco to achieve record power density.
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Single-agent supremacy is real, but recently I’ve been leaning into having a verification agent whose only job is to tie everything agent 1 says back to the original source. Also I’m exploring IRAC as a verification framework, so far so good.
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The surprising lesson from a seven-agent litigation system my Stanford students built: it was overstaffed.
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Sean Latt retweeted
Tesla VP of Hardware Engineering Lars Moravy explains to Hagerty how the Roadster borrows SpaceX cold gas thrusters to make 0 to 60 possible in 0.55 seconds. Truly insane: ▫️ 200L carbon-over wrapped pressure vessel behind seat holds nitrogen at 10,000 PSI ▫️cold gas thrusters push out 80 kN of thrust (equivalent to a single fighter jet engine) ▫️Tesla went with nitrogen because “it just seemed wrong to put fuel into a car” (air is ~80% nitrogen, fair enough) ▫️thrusters same system satellites use to move in space (eg. the “psh psh psh" from Apollo 13 film) ▫️currently takes 6 to 7 hours to refill all nitrogen but Tesla working for it to be 30 minutes ▫️uses leg actuators from Optimus robot to manage downforce on wing *** Full 25-min episode with Henry Catchpole here: youtu.be/22rvpUecLts?si=jtwK…
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A retired NASA engineer who had worked on the Space Shuttle program for 25 years was hired by SpaceX at age 62. He described the first week as the most disorienting experience of his professional life. At NASA, a design change required 3 meetings, 2 committees, a review board, and 6 to 18 months of documentation. At SpaceX, his new supervisor said "if it works, build it. If it doesn't, change it by Friday." He sat at his desk after the first day and called his wife. He told her he felt like a man who had been swimming with ankle weights for 25 years and someone just cut them off. He could move. He could decide. He could build something in the morning and test it in the afternoon. The constraint that he had accepted as the nature of aerospace engineering turned out to be the nature of bureaucracy. Within 6 months he had contributed to more design iterations than he had completed in his last 5 years at NASA. Not because he was working harder. Because the time between having an idea and testing the idea collapsed from months to hours. The same brain. The same knowledge. A different operating system. He described a specific moment where he proposed a modification to a Raptor engine component. At NASA, the proposal would have entered a queue. At SpaceX, the modification was machined by the end of the day and tested by the following morning. He watched his idea become a physical object in 18 hours. At NASA, 18 hours would not have been enough to schedule the first meeting to discuss whether to schedule a meeting. He told an interviewer: "I didn't get smarter when I went to SpaceX. I got faster. The intelligence was always there. The system was in the way." The difference between SpaceX and NASA is not talent. NASA has extraordinary talent. The difference is the time between a thought and a test. SpaceX compresses it. NASA elongates it. The same engineer produces 10x more at SpaceX than at NASA because the bottleneck was never the engineer. It was the meetings about the meetings about the work.
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Sean Latt retweeted
Wild how many investors only want in once a company is famous. Famous = expensive. Early = where the returns are. At Anti Fund we hunt early.
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What are the long term consequences of American lawyers adopting Chinese open weight models (like glm’s new legal llm). Are there implicit bias baked in? Open weight isn’t open source so a black box still exists, should be fun to watch this play out. @NousResearch should play ball in this realm.
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