Full-stack engineer. What does it cost to live in the EU? https://xitter.cf/t.co/pgvIA34S2N Looking for European Podcasts Charts? https://xitter.cf/t.co/Qg2gkBLUwV

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Joined October 2022
# AGENTS.md - Remove obsolete paths. Ship only current code. - Use simplest code that meets needs now. - Build in layers. Ship smallest working version first. Add on top of working product. - Keep modules separate with clear concerns. - Prefer mature libraries that simplify or stabilize. - Check existing deps, docs, and types first. - Design for the long term. - Study proven products. Adopt their patterns.
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a grounded take on the AA (v. 5) index
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the new artificial analysis intel index (5) is brutal for open-weights models Claude Opus 5.5 (medium) scores 48 with just 43M tokens. DeepSeek v4.1-flash (max) scores 32 with 150M tokens xitter.cf/ArtificialAnlys/status…
Announcing Artificial Analysis Intelligence Index v5, featuring 2 new benchmarks and 3 upgrades. For the first time, the majority of the Index is built on private datasets The Artificial Analysis Intelligence Index is our composite measure of model capabilities across tasks reflecting real-world usage today. v5 is our biggest upgrade since v4 launched in January. With v5, 60% of the Index is now based on evaluations with private test sets or held-out answers. Alongside Intelligence Index v5, we’re launching the Artificial Analysis Progress Index v1, which tracks model capability over time on one continuous scale. New to the Intelligence Index: ➤ FrontierCode 2.0: Cognition's newly-launched private benchmark of real-world coding tasks, graded on whether a repository maintainer would merge the agent's change ➤ Terminal-Bench-Science: 70 agentic scientific research tasks, run in a terminal environment Upgraded: ➤ HLE-Diamond (replacing Humanity's Last Exam): a refined 1,000-question version of Humanity's Last Exam, split evenly between reasoning and knowledge questions ➤ AA-Omniscience v1.1: An updated dataset with saturated and public set questions removed, new questions added, and ambiguous or erroneous questions revised ➤ CritPt v2: a private, 40-question hard subset of CritPt v2, revised by the CritPt authors and run with a code-execution tool Staying in the Index: AA-Briefcase, AutomationBench-AA, GDP.pdf and Terminal-Bench 4.0 Removed from the Index: GDPval-AA v2.1, SciCode, and AA-LCR v1.1 Key results: ➤ Claude Opus 5.5 (max) leads at 58, ahead of GPT-6 Astra (max, 57) and GPT-6.1 Sol (max, 55): Anthropic and OpenAI hold the top five models. Gemini 4 Argon (high) is next at 50 ➤ The open weights gap widens from 3 points to 12: Kimi K3.1 Pro (weights not yet released) is the top open weights model at 46. Among released weights, DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) lead at 32 - 26 points behind the leader and below Claude Haiku 5.5 (max, 36) ➤ Cost per task varies by more than 13x among the leading models: of the 10 highest-scoring models shown, GPT-6.1 Terra (max) is the cheapest at $0.96 per Intelligence Index task and Claude Fable 5.1 (max) the most expensive at $13.04
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small (700b) open-weights models are great at coding and tool calling but lack world knowledge. because the parameter difference is so huge T to B, of course they also are not as intelligent. this is i think the difference in this new analysis.
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LFM3 1.7b running at full context (262k) on 6gb vram card. 91 tok/sec speed
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Model is in Q8 from my tests these small models degrade too much under Q8
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asked it for a story about a donkey in a nice html page
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lets see what the new LFM3 can do, its only 1.85gb in size for Q8. 128k context takes around 1.6gb more. so just around 3.5gb of VRAM/RAM needed to run this model. xitter.cf/liquidai/status/211512…
Introducing 𝙻𝙵𝙼𝟹: our new generation of Liquid Foundation Models. With a new tri-hybrid architecture and new multimodal encoders, the LFM3 family is the ideal model suite for specialized intelligence. > 6 model instances, across text, audio, vision, and omni modalities, generative and decision models. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱: text, 256k context window. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱: vision-language model with video-input support for spatial understanding > 𝙻𝙵𝙼𝟹-𝙰𝚞𝚍𝚒𝚘-𝟸𝙱: tool calls, voice chat, reasoning in 18 languages. > 𝙻𝙵𝙼𝟹-𝙾𝚖𝚗𝚒-𝟸𝙱: an omni model combines vision, audio, and language. > 𝚍𝟷-𝟸𝙱 and 𝚍𝟷-𝙾𝚖𝚗𝚒-𝟸𝙱: decision models with typed outputs and calibrated probabilities in a single pass. Omni adds audio input. > fine-tune on 1 GPU. > a new tri-hybrid architecture > architecture optimized for 14 processor classes across GPUs, CPUs, and NPUs. > 𝙻𝙵𝙼𝟹-𝚅𝙻-𝟸𝙱 is a beast. It performs competitively with Gemma 4 12B (6x larger), and Gemini 3.5 Flash Lite, and GPT-5.6 Luna, with 1.9B parameters. > 𝙻𝙵𝙼𝟹-𝟷.𝟽𝙱 is #1 on the Pareto frontier of device intelligence 𝚙𝚒𝚙𝚎𝚝𝚝𝚎.𝚕𝚒𝚚𝚞𝚒𝚍.𝚊𝚒 Every modality for specialized intelligence. Pick the one that fits your use case, customize it, and run it on-device. 1/ 🧵
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i gave it this prompt and the result.. is not great but is error free and working: "write a one page html page with a story of a horse in a barn and make it a nice page with effects" sourcecodeplz.github.io/mode…
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8k tokens limit reached just at the end of the run and it did 97.85tok/sec
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seems the GGUF from @liquidai is not working in @lmstudio latest version (0.4.26) windows 11
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going to Settings > Runtime > Check for updates (runtime updates channel) fixed it
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when did chatgpt start talking like this? lmao
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what a car this was at $40k new
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