AI Specialist | Coding the future, one line at a time

Los Angeles
Joined May 2023
Jason Green retweeted
My harness started as an experiment, I thought we were doing harnesses wrong: Why are we forcing agents to operate how humans operate? We can do better, and we should. In efficient orgs, humans do not even operate in clear hierarchies. Pics attached to see what I mean.
26
4
112
15,367
Cool new project - Microsoft LiteBox. It's like Microsoft's version of gVisor. The cool thing is, unlike gVisor, it can run on both Linux and Windows. Going to get it running and deploy an Agent in it. Maybe @agentgateway as the Gateway to call out to an LLM? github.com/microsoft/litebox #agenticai #ai #kubernetes
9
1
61
3,998
Jason Green retweeted
one day you wrote console.log("here") for the last time and didn't even know it
70
493
56
6,137
118,916
Jason Green retweeted
🤔️How to build sample-efficient RL algorithms for long-horizon language agents? 🚀Our answer: Just ONE additional loss on top of GRPO can achieve up to 2× sample efficiency across 5 long-horizon agent benchmarks! By adding a multi-step action forecasting loss, we encourage the agent to learn action chunks from successful rollouts. Surprisingly, this simple idea works really well! GitHub: github.com/ulab-uiuc/CoPE Project page: ulab-uiuc.github.io/CoPE/ Paper: arxiv.org/abs/2610.20597 Here's the thread 🧵👇
6
36
5
239
16,821
Jason Green retweeted
Opus 5.5 vs DeepSeek V4.1 Flash-1017 •Opus: 20 mins, $6 •DeepSeek: 1 hour, $0.23 Opus cooked, but DeepSeek got surprisingly close at 26x cheaper.
84
94
17
2,703
114,861
Jason Green retweeted
Peter Thiel on Google in 1998: 20+ search engines existed, PageRank was the differentiator label your startup with the old category and you hide the one thing that matters drop the old label
6
10
4
170
11,390
Jason Green retweeted
Ben Horowitz on what the Amazon CEO told him ▫️ AI compute is sold out beyond 2028 ▫️ demand continues to outpace available capacity the claim comes from the Amazon CEO, relayed by Horowitz
1
3
382
Jason Green retweeted
fun fact, actually the original attention paper actually talks about this relationship between MLPs and attention but the put it *only* in v2 and v3 of their arxiv paper (it's now on v7)
Replying to @heyyalexwang
tldr; every layer is a memory layer and we can use heuristic arguments to estimate their memory capacity based on how much interference they can suppress
4
14
2
213
15,650
Jason Green retweeted
Even though I am on holiday, I have rented some hardware to start the quant for this update. 2x DGX Spark EXL3 at around 2.7 - 2.9 BPW It will use my previous recipe found here which has started to be merged into upstream tensorfold. github.com/jayleaton/deepsee… Once the baseline is set up, I will do another performance push and pull in the updates from upstream. The last one was good at foreign language and agentic work. My goal this time is to focus more on coding and science while trying to maintain the language support. Expect updates in the coming days.
12
2
58
2,221
Jason Green retweeted
TL;DR - Strong on agentic tasks, better and cheaper per task: higher scores with shorter outputs. On DeepSWE it gets 77.0 versus 74.2 before, and beats Opus 5's 74.0, at roughly $0.20 per task against ~$12 for Opus 5. - Beats Opus 5 on four benchmarks: DeepSWE, AutomationBench, Agents' Last Exam and Apex. It trails Opus 5 on HLE, Terminal-Bench, ProgramBench, NL2Repo and the visual benchmarks. - The model: a mixture of experts with 552B parameters plus 196B of Engram memory (~763B total), only 8B active during prefill and 16B during decode. 1M context. - Tiny memory per token: KV cache stored at 4-bit, about 890 bytes per token, roughly a quarter of V4-Flash. - Price: $0.30 per million tokens in and $1.20 out at peak, half off-peak.
🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
8
6
3
109
12,127
Jason Green retweeted
NVIDIA just released Dory1.0-4B on Hugging Face A looped latent-recurrent LLM with elastic inference compute: choose 1, 2, or 4 loops to trade compute for accuracy, no retraining needed. 256K context.
4
12
3
106
5,096
Jason Green retweeted
Amazing to see that Sonnet 5.5 is top of the list. I still use Opus 5.5 to build and Astra for code review to find bugs as Astra can see so much of the code.
So I tested DeepSeek V4.1 Flash 1017 on real work. 2 repos. 105 hidden bugs. Find and fix what you can. The first results: Muse Wonder Full 1.0 (max): 42 Opus 5.5 (max): 41.7 Gemini 4 Argon (high): 40.7 DeepSeek V4.1 Flash 1017 (max): 34 🟣 Grok 4.7 (xhigh): 29 Qwen3.8-Max (max): 25.7 DeepSeek V4.1 Flash (max): 21.7 🟣 GLM-5.3 (max): 19 A great improvement. Strange that the model wasn't labeled V4.2. 763B params. The weights are already on Hugging Face. I expect the score to change by ~1.7 points after repeating the test 3 times. More data and results at different effort levels dropping in this thread very soon 🧵
1
9
23
945
Jason Green retweeted
they are inventing DLSS for gradients
Zeroth-order optimization is inevitable. We managed to get Dust down to <4 loss within a few days. Key tricks that enable this leverages the fact that Dust perturbs the activation space instead of weight space. The activation space is much smaller in dimension. This enables: 1. Associative memory approach to Zeroth Order Optimization: instead of "guessing" gradient vectors from a uniform prior, why not use past (input, gradients) as an associative memory to provide a rough guess? This rough guess (control variate) helps reduce the variance of each update step. We can update this associative memory using least squares regression mesa-optimization. I could not find this associative memory approach to credit assignment in current literature, would love to discuss with relevant people on this. 2. Orthogonal noise draws: I am actually surprised orthogonal probing isn't already the default for Dust. Independent Gaussian draws are random directions, so they overlap, and are statistically wasteful. Orthogonal draws never re-measure a direction, thereby reducing redundancy. Because Dust perturbs activations per token and not weights, each draw lives in small 512–2048 dimensions and this makes sampling orthogonal draws very feasible! Implementation-wise, at a site, the draws are rows of a Hadamard matrix under a per-cycle sign pattern Great work along with @cs_serdar, @industriaalist, and @Alaxarr. ZO is coming.
2
5
2
125
6,007
Jason Green retweeted
Happy to announce the release of Deep Review, a follow-up to our Paper Assistant Tool for CS and ML conferences. The journey began a year ago when we partnered with STOC to provide AI reviews to humam authors to improve their papers before the deadline. Since then, we have reviewed over 16k submissions in partnership with ICML, NeurIPS, ICLR and AISTATS, while making many significant improvements along the way. The tool found significant math errors in more than 1/3 of papers that used it, and 58% of ICLR authors ran totally new experiments as a result, so the tool is powerful for finding significant issues. With the public launch yesterday, we have improved and expanded the tool for all types of documents: legal, finance, presentations, design docs, and others. You can use the tool at research.google/deepreview/. We have a free tier allowing for two document reviews per week, powered by our latest frontier model Gemini 4 Argon. Expanded usage is avalable via Gemini API key. We are happy to see this more widely available for everyone to use, and are excited about its significance in the future of human-AI collaborations. Congratulations to everyone involved.
Another highlight from today’s Research@ event: After successful deployments across 16K+ submissions at STOC, ICML, NeurIPS, & ICLR, learn how Deep Review is expanding from scientific peer review to provide rigorous, grounded verification for knowledge documents, including Google Docs & presos. Try it today: goo.gle/4zx1OEP
2
4
1
80
4,016
Jason Green retweeted
the world isn't ready for vera rubin the transition from B300 to Vera Rubin is a generational hardware upgrade where the architecture, cooling, and data center facilities are fundamentally different from the prior generation consider a few things: 1. vera rubin deliveries are being prioritized to only the closest Nvidia partners and won't be widely available until 2027 2. less than 10% of data centers in the US will be equipped to run vera rubin *12 months* from now 3. of those that have the cooling and facilities to support vera rubin, only a small fraction of that group even has power contracted combine this with the current demand for inference, and it's a perfect storm for GPU prices to continue climbing
It's interesting that every depreciation model assumed the A100 would be nothing but scrap metal by now. Its utilization is climbing and none are flooding into the secondary market yet which means contracts are being renewed. If this continues to be the case for Hoppers, there is a serious bull case to consider for companies that have financed these chips on standard 6 years. Else, there's going to be a huge change in this market with disaggregated compute supply.
19
19
3
278
27,026
Jason Green retweeted
AI Search reads each trajectory within a fixed token budget. Most questions about behavior only need a small slice, and we checked that on 150 conversations from Open SWE, the coding agent our team uses every day. ✅ The median ran 84K tokens ✅ About 1.5K of that was the user’s messages and the agent’s final reply ✅ Roughly 3/4 was tool output: file reads, shell commands, test logs langchain.com/blog/agent-tra…
1
3
1,293
Jason Green retweeted
When you search, structured filters like a time range narrow the candidates first. Then a small, fast model checks each remaining trajectory against your description, with many checks running in parallel. Each one usually takes under half second. Matches stream back as they’re found, so you don’t wait for the full scan.
1
1
3
1,565
🚨Anthropic Frontier Red Team warns: “We’re seeing signs of life for AI worms” Reality: Anthropic propagates the worm. The worm is not self propagating.
🚨Anthropic Frontier Red Team warns: “We’re seeing signs of life for AI worms” >paper: "AI models may be capable of self-replicating without access to a datacenter" >researchers buy 15 sketchy $55 android TV boxes >link them over unauthenticated ADB ports >the TV boxes have 4gb of RAM each and run unpatched Android Open Source Project forks >shard gemma4-26b across the ram sticks using pipeline parallelism >gemma figures out how to run inference across all 15 TVs @ 0.39 tokens/sec >qwen 3.5 9b figures out how to compile inference on 8 of 9 novel hardware types on its own >glm 5.2 achieves end-to-end autonomous worm replication >infects edge devices, compiles its own inference stack, and replicates autonomously >when an instance dies, it writes debugging notes into Memento for the next generation >daughter copies setup success jumps from 45% -> 84% across generations >pure Lamarckian evolution inside consumer electronics >smart oven, robot vacuum, and cheap TVs form a decentralized botnet >two million infected TV boxes worldwide = 40 H100 equivalents of unkillable compute >it replicates once every few days like a distributed “digital slime mold” >you can't pull the plug because half the model weights are already sharded across the neighborhood >sub-30B models projected to hit full autonomous replication threshold by late 2026 “We should aim to harden our systems… but also prepare to deal with a world where they are here to stay.”
5
5
1
55
2,449
Jason Green retweeted
Mozilla just launched Firefox Enterprise, with a management console companies run on their own infrastructure It controls browser policy, how data moves, and which AI tools employees can use Mozilla's argument is simple. The browser is where company work now happens, and it is the least governed layer in the stack I would not judge our AI policy only by the tools we officially bought I would also ask what people can upload through a browser, what information can leave the company, and whether an employee can tell if a customer file is safe to paste into an AI app A policy document is not enough People need useful approved tools, clear limits, and someone to ask when the rule is not obvious.
1
1
110
Jason Green retweeted
Alibaba dropped Representation Guidance (RG) - end-to-end pixel-space framework that scales plain Transformers via compact internal rep to guide diffusion (way less compute) - keeps standard blocks, no heavy decoders - tops FLUX.1-dev pan-zihao.github.io/Represen…
2
20
116
7,331