Охват к подписчикам
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Реакции к просмотрам
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25 на 20 постов
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134
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всего 20
Где отзываются чаще
доля реакций к просмотрам- 31 июл.[llm][harness] https://lilianweng.github.io/posts/2026-07-04-harness/2,07%
- 14 янв.[#AI #LLM #MachineLearning #EdgeComputing] Running LLMs with Just SQL? New Research Makes It Possible Researchers dropped TranSQL+ - a wild approach to run Large Language Models using only SQL queries in a regular database. No GPUs, no fancy ML frameworks needed. The idea: Convert the entire LLM (like Llama3-8B) into SQL queries that run in DuckDB or similar databases. Sounds crazy, but it actually works! Results on a modest laptop (4 cores, 16GB RAM): 20× faster than DeepSpeed for first token 4× faster for generating subsequent tokens Works entirely on CPU with limited memory Why this matters: • Runs on ANY device with a database (phones, laptops, IoT) • No need to compile for different hardware • Databases already know how to manage memory efficiently Perfect for privacy-focused edge deployments where you can't rely on cloud GPUs. Paper: https://arxiv.org/pdf/2502.028181,15%
- 20 янв.[#ai #engineering] I think it’s important to watch this short video of a Netflix Staff engineer about usage of AI/LLM and perception society puts into that. There are some learning I wish more people could understand. Thank you and I wish you all a great day! https://youtu.be/eIoohUmYpGI?si=9A2q5kxelLZy7L5N0,88%
- 17 июл.[llm][skills and evals][video] Great overview with tips for writing skills from Google Deepmind engineer: https://www.youtube.com/watch?v=0vphxNt4wyk0,74%
- 15 янв.[#AI #AICoding #AIAgents #AutonomousAgents #MultiAgentSystems #Cursor] Scaling AI Coding: Lessons From Running Hundreds of Agents Cursor shares how they pushed the limits of AI by running hundreds of autonomous coding agents at the same time on real software projects. Instead of short tasks, these agents worked for weeks, edited shared codebases, and even helped build complex products like a web browser. The biggest lesson? Uncoordinated agents create chaos — but a planner + worker system keeps them aligned, focused, and productive over long periods. The article shows that with the right structure, AI teams can tackle massive engineering challenges, similar to real human teams — and we’re just getting started. 🔗 Read more: https://cursor.com/blog/scaling-agents0,48%
- 26 февр.[llm][research] “We show that large language models can deanonymize users at scale. With internet access, our agent can re-identify pseudonymous Hacker News and Anthropic Interviewer users with high precision—matching hours of human investigation. In a closed-world setting, we build a scalable LLM pipeline that: 1. extracts identity clues from raw text, 2. finds candidate matches via semantic search, and 3. verifies matches to reduce false positives. Unlike prior work requiring structured data, our method works directly on unstructured content across platforms. Across three datasets (HN↔LinkedIn, Reddit↔Reddit communities, and split Reddit histories), LLM methods vastly outperform classical baselines—up to 68% recall at 90% precision vs. near 0% for non-LLM approaches. Bottom line: pseudonymity online is far more fragile than assumed, and privacy threat models need updating.” https://arxiv.org/pdf/2602.168000,32%
- 9 мар.[ai][multi-behavior brain] https://x.com/alexwg/status/2030217301929132323?s=46&t=eNN3Y-GKeBSlFyyj1ozvgg0,31%
- 9 июл.[ai][world models] It’s been a while. I feel sorry for not sharing much within a 3 months period. I’ll try to get it going. Recently watched Yann LeCun presentation explaining World Models concept. Seems the deeper researched dig the further AI hype goes. Although it’s quite interesting! https://www.youtube.com/watch?v=72Xj8k5WQX40,23%
- 12 июл.без подписи0,23%
- 11 авг.[challenge][reverse engineering] https://blog.janestreet.com/can-you-reverse-engineer-an-asic/0,00%
- 3 авг.[llm][pelican on the bicycle] https://simonwillison.net/2025/Jun/6/six-months-in-llms/0,00%
- 14 апр.[ai][layoff][paper] “If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and “better” AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity participation, universal basic income, upskilling, or Coasian bargaining. Only a Pigouvian automation tax can. The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it. “ https://arxiv.org/html/2603.20617v10,00%