Not always as serious as expected... ^^ Freelancing đŸ€“

Joined February 2009
freiden retweeted
Cette voiture est fabriquĂ©e avec du lin ! 🚗 Au Mondial de l’Auto, la start-up espagnole Liux prĂ©sente la BIG, une voiture Ă©lectrique dotĂ©e d’une coque en fibre de lin. Cette structure serait plus Ă©cologique avec 60% d’émissions de CO2 en moins Ă  la fabrication. đŸ“č Emmy Audrain
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freiden retweeted
Nous, médias, avons une responsabilité dans la montée du racisme au sein de la société. @SNJ_national @CGT_RMC_BFM
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freiden retweeted
Toutes les images qui viennent de Montpellier, de Rouen et de Nice, et qui montrent trĂšs clairement la complicitĂ© entre la police et des groupes nĂ©onazis, sont tout simplement scandaleuses. Il faut exiger la dĂ©mission immĂ©diate de Nuñez ! Au lendemain d’un attentat Ă  Trappes, lors duquel le terroriste d’extrĂȘme droite aurait revendiquĂ© avoir « vengĂ© les commerçants » le 17 octobre, la police a trĂšs clairement protĂ©gĂ© ces nĂ©onazis dans les centres-villes de Montpellier, de Rouen et de Nice, leur permettant d’attaquer les manifestants. C’est tout simplement scandaleux. À Montpellier, on voit mĂȘme un manifestant mis Ă  terre par les CRS tandis que des nĂ©ofascistes continuent de lui donner des coups de pied Ă  la tĂȘte, en toute impunitĂ©. Sur d’autres images, on voit les CRS charger les manifestants tandis que les nĂ©onazis restent Ă  leurs cĂŽtĂ©s. Pire encore, ces derniers profitent de la prĂ©sence des CRS pour continuer Ă  frapper, avant de repartir tranquillement. Il faut en faire une affaire d’État et ne rien lĂącher lĂ -dessus !
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🚹 De nouvelles vidĂ©os Ă  Nancy dĂ©montre d’un mĂȘme systĂšme opĂ©ratoire que dans d’autres villes comme Montpellier, Nice ou Dijon 1. Les fascistes attaquent des manifestant‱es barres Ă  la main devant la police 2. Lorsqu’il y a riposte, la police charge et dĂ©fend les nĂ©o-nazis
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There are many discussions on unit tests lately, so I recorded a quick video sharing a side-project I am working on, how it is structured, and where unit tests fit. It was an impromptu session while I was working on something else, so a bit chaotic but hopefully useful.
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freiden retweeted
đŸššđŸ‡«đŸ‡· FLASH | Kevin, le TERRORISTE responsable de l’attentat raciste de Trappes, votait Ă  l’EXTRÊME DROITE. (RMC)
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Incroyable manipulation de @cnews qui diffuse des images d’Albanie en faisant croire que c’est à la porte d’Italie à Paris. Il faut fermer cette chaüne de propagande. Que fait l’@Arcom_fr ?
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Check out @liquidai d1 decision model running locally in LM Studio. Blazing fast! It's playing a custom version of Dino Run (and doing better than me at it).
State → local decision model → action Check out @liquidai's d1 decision model playing Lmmy Run, all on its own đŸŠ–đŸ‘Ÿ Served 100% on-device, making decisions in a fraction of a second
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freiden retweeted
anthropic built their own git. Most interesting mental shift here, and if you buy that changing code happens more and more autonomously, faster*, then this will make a ton of sense: Git was designed so that any client could serve the repo. Fully offline, your *build time* dev stack is never in the hot path for your *run time* deployment. Anthropics new version control system (VCS) does not work offline. There’s a central server which must be reachable for clients to function. They found a bunch of benefits to doing it this way. At first this sounds wild. Today if GitHub is down, you can’t pull or push or merge. Imagine if you couldn’t COMMIT if GitHub was offline. But in a (someday, maybe) future where code changes 1000x per day with no human in the loop, then your VCS is basically part of production anyway, so you might as well lean into keeping it online with a team of SREs etc, and then you get all sorts of interesting perf benefits for CLIENTS because you can offload some processing to server and re architect what all needs to be stored where * I’m not sure I buy this, but the labs are certainly pushing “don’t read the code, leverage signals like “outage, feature request, user behavior change” as STIMULUS to agents to drive change in deployed code” as the future, so it lines up Does that make sense? Are we going back to CVS instead of distributed? Will there be another swing back to distributed 5-10 years after things like Scion become Mainstream?
New on our Engineering blog: Creating in-house source control. In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale. Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: anthropic.com/engineering/re

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freiden retweeted
All my oQe models have been updated with Improved oQe Quantization, available from oMLX 0.7.1. Compared with the previous oQe builds, KL divergence is 11.6–18.1% lower on code and 10.2–13.8% lower on prose, so please give them a try. The full model list is below. huggingface.co/Jundot KL divergence from the original BF16 model (mean, lower is better) - Qwen3.8-27B oQ4e: code 0.0143 → 0.0117 (-18.1%), prose 0.0696 → 0.0600 (-13.8%) - Qwen3.8-Flash-Next oQ4e: code 0.0877 → 0.0776 (-11.6%), prose 0.1909 → 0.1713 (-10.2%) What's new - Below oQ4, the Engram and N-gram tables stay at 4 bits and are left out of the bit budget. oMLX can serve these tables from SSD instead of memory, so the full bit budget goes to the rest of the model. - Model cards now show the bit distribution and storage breakdown. If you downloaded an earlier oQe build, please re-download it. Improved oQe models (69) - Qwen3.8-Flash-Next: 2.5 · 2.7 · 3 · 3.5 · 4 · 6 · 8 bits - Qwen3.8-Flash-Next FP16: 2.5 · 3 · 3.5 · 4 bits - Qwen3.8-27B: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - Qwen3.6-35B-A3B: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - GLM-5.3-Flash: 2 · 2.5 · 3 · 3.5 · 4 · 5 · 6 bits - DeepSeek-V4.1-Flash: 2 · 2.5 · 3 · 4 bits - DeepSeek-V4-Flash-0731: 2.5 · 2.7 · 3 · 3.5 · 4 bits - gemma-4-31B-it: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - gemma-4-26B-A4B-it: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - gemma-4-12B-it: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - clef: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits - clef-flash: 2.5 · 3 · 3.5 · 4 · 6 · 8 bits
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40 years ago today, ‘Saint Seiya’ began airing on ANN.
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On this day 40 years ago 🌠 Episode 001: よみがえれ! 英雄䌝èȘŹ "Revive! Legendary Hero" What's you best memory? Let's go back to 1986 and re-watch the serie together! Every week we revisit, share opinions and feelings about to the latest episode! #è–é—˜ćŁ«æ˜ŸçŸąă‚ąăƒ‹ăƒĄćŒ–ç„40摹ćčŽ #SaintSeiya #è–é—˜ćŁ«æ˜ŸçŸą #SaintSeiya40th #è–é—˜ćŁ«æ˜ŸçŸą40th
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Cette BĂ©rĂ©nice Levet, qui lance des sourires complices Ă  Mathieu Valet du RN quand l’avocate Elsa Marcel dĂ©nonce les violences policiĂšres, pour lui faire comprendre qu’elle est du cĂŽtĂ© des flics. N’assume pas quand Elsa Marcel lui rappelle qu’elle Ă©crit dans Valeurs actuelles 🙃
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freiden retweeted
"ce mouvement est soutenu par 3 français sur 4, sur ce plateau vous vous retrouvez majoritaires avec 3 personnes de droite qui vont nous expliquer que les lycéens sont des délinquants" Bravo Elsa Marcel ! Faut avoir le cardio pour supporter ce plateau et leur claquer leur 4 vérités comme ça !
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For Those Who Come After! The Expedition 33 universe is finally landing into the Prison. Our team brought the beautiful world of @expedition33 to life in pixel art: iconic characters and new boss fights are waiting for you NOW in Astral Ascent.
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J'ai pas les mots tellement j'ai mal aux yeux 😭
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freiden retweeted
releasing my local AI slide deck covers from prefill vs decode, MoE vs dense, VRAM vs unified memory, quantization to speculative decoding, everything with llama.cpp feel free to reuse with attribution
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freiden retweeted
You can now train your own Decision model like Jev locally! We increased Qwen3.5 0.8B’s aggregate accuracy from 20.7% to 74.3% across 3 decision benchmarks - on just 4GB VRAM. Turn any LLM like Qwen3.8, Gemma 4 into decision models with our open-source Unsloth repo. We fine-tuned with a Clef head using Unsloth and LoRA (r=64) for one epoch, increasing downstream accuracy from 30–37% to 78%. GitHub: github.com/unslothai/unsloth Guide and Notebooks: unsloth.ai/docs/basics/train

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