Data Engineering | DataScience | AI & Innovation 🚀 🚀 CTO of ELITIZON https://xitter.cf/t.co/qkVpOCgY0m 🤖 Co-Founder of QuantaLogic https://xitter.cf/t.co/f9qamWSbjE

Hong Kong
Joined March 2010
Truth is Expensive: Why RAG is Doomed and What Actually Matters You're not building a truth machine. You're building the world's most efficient fiction amplifier. That's not a bug. It's the inevitable result of solving the wrong problem. Yuval Noah Harari's core insight—delivered in his latest book Nexus and his viral TED thesis—is brutally simple: Humans are a post-truth species. We didn't conquer the planet by being right. We conquered it by believing in shared fictions: gods, nations, money, brands. These stories don't need to be true. They need to be believed. [1] And now we're hardcoding that vulnerability into AI.
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Seriously mindblowing: This voice model runs locally on an M4 Max Mac or better. Feels like magic.
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence. block.xyz/dessa
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We're one week away from the ScienceClaw Hackathon! Thank you also to our incredible sponsors and supporters for making this possible: @nvidia, @AnthropicAI, @formlabs, @romixbio, @MITEngineering, @askalphaxiv and others. We are looking forward to three stimulating days of building, experimentation and scientific discovery - exploring how decentralized collectives of AI agents can tackle challenging problems across science and engineering! October 30-November 1, 2026, MIT Media Lab scienceclawhack.ai/
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created a live agent trace waterfall that you can zoom and inspect 🔍 → kobra.systems/trace-waterfal…
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同一台 Mac 跑本地大模型,69 tokens/s 和 161 tokens/s 的差距,居然来自推理引擎。 开源项目 Kapitsa,专门针对 Apple Silicon 优化。 官方测试:M4 Pro、48GB 内存,运行 Qwen3.6 35B-A3B(4-bit): llama.cpp:69 tokens/s Ollama:89 tokens/s Kapitsa:161 tokens/s 核心是 KFlash,通过树状推测解码,一次验证更多候选 Token,减少重复计算。 还支持: Claude Code、Codex、OpenCode 接入本地模型 OpenAI / Anthropic 兼容 API SSD 缓存,重启后复用上下文 多 Agent 并发推理 安装只需: brew install nace-ai/tap/kapitsa 数据来自项目自己的 Benchmark,实际速度取决于 Mac 配置。 Mac 跑本地 Coding Agent,值得拿来和 Ollama 实测一轮。 GitHub:github.com/nace-ai/kapitsa #AI #Agent #LLM #ClaudeCode #Codex #AppleSilicon #OpenSource
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HOLY MOLY. Something genuinely strange just happened in AI training. Q Labs says it can now pretrain a transformer WITHOUT backpropagation, cutting the time to reach a fixed validation-loss target from 1.76 hours to just 1.6 seconds in only 10 days. A nearly 4,000x speedup. The latest run also beats backprop + Adam on validation loss. Its Dust algorithm was already estimated to be up to 10,000x more compute-efficient than the previous state-of-the-art zeroth-order method. The biggest question now is scaling. Some of the tricks won't scale, so the team is moving from 10M to 30M training tokens.
I think the last month might have been the biggest month in (zeroth-order) optimization history. In the 10 days since we released the Noise speedrun, the time to pretrain a transformer went from 1.76 hours to 1.6 seconds, a 4,000x (!!!) speedup. Dust itself was up to 10,000x more compute-efficient than the previous state-of-the-art. Not only can we pretrain a transformer without backprop, it's much more efficient than anyone would've expected. Search fking works! Now, scaling is the biggest open question. Some of the tricks that brought the time down to 1.6 seconds will simply not scale. To discard those, we'll be scaling the dataset every time it gets overfit, starting with 30M tokens instead of 10M. Huge thanks to @jayden_teoh_, @spicey_lemonade, @RobertTLange, @EmanueleRodola, @ChunyuanDeng, @LuochengHuman, @Alaxarr, @pavleculafic, @IsaacYong13, Euraba and areu01or00 (GitHub), for the contributions. And @cs_serdar for gracefully maintaining the speedrun and @bishmdl76 for helping out. Expect a lot more from us over the coming weeks.
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earshot v0.4 is out! added: - 51 new sounds, all made with code (252 now) - cut hits, knocks, drips, shutters, chimes, UI pops and reverse swells and more - a new launch kit: so the sounds will be right on the cut and above the music + a website where you can play every single sound >> leonxlnx.github.io/earshot-s… all the sfx in this video are made with earshot :)
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deep Manifold @BetaTomorrow さんの論文解説(図解)の大ファン。だから,自著論文の論文解説(図解)が登場して嬉しかった alphaxiv.org/abs/2610.14012 xitter.cf/BetaTomorrow/status/21…
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🚀 Introducing Next-Concept Modeling (NCM): our new paradigm for fast, parallel, coarse-to-fine text generation. Compared to strong diffusion language models (DLMs) at similar parameter size, NCM achieves: ⚡ Up to 31× faster generation on downstream tasks like summarization and machine translation with comparable or better quality. 🏎️ 10–21× faster unconditional generation across TinyStories, LM1B, and OpenWebText, with comparable or better generative perplexity and using 3–5× fewer training tokens. 🧩 Better performance on reasoning tasks like Sudoku, outperforming both AR and diffusion baselines with fewer sampling steps. With @ishanskhare, @IddahMlauzi, @ekellbuch, @simran_s_arora, @Azaliamirh, and @HazyResearch. More details in the thread 🧵👇
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i've been a bit quiet - working on a new companion skill that will help your agents draw these beautiful statecharts/diagrams + check them against the specs you made with /create-formal-verification-skill in pstack! it now uses Pele from @kepano too which is why they look so good. will release soon!
there's something really cool about asking your agent to draw a statechart (hypothetical example in this image), then using the /create-formal-verification-skill you made with pstack to find bugs through something like TLA+. you can both understand your code better and how the user flow should work, and fix issues users would otherwise see
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The lectures of my new course "Principles of Deep Representation Learning" are now available at the Youtube site: youtube.com/playlist?list=PL… with new lectures continuing to be added there. I will open-source all the slides later in the textbook website: ma-lab-berkeley.github.io/de…
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💡 (1/n) How do scientific ideas 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐞𝐯𝐨𝐥𝐯𝐞 from an initial question to a published discovery? We introduce the 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬 𝐆𝐫𝐚𝐩𝐡 (𝐑𝐏𝐆), a new representation to model 𝐚𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐭𝐫𝐚𝐣𝐞𝐜𝐭𝐨𝐫𝐢𝐞𝐬 that human experts do. It captures how ideas evolve through experiments, insights, failures, and revisions—and crucially, the reasoning behind every transition. Existing approaches either generate research ideas in a one-shot manner or rely on artificial workflows that capture only fragments of the RPG. 📄 arxiv.org/abs/2610.27337
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A San Francisco startup says it just turned mercury into real gold, and their own math says gold is one of the cheapest things their machine can make. Aim it at a different metal and it makes a cancer drug ingredient worth tens of billions of dollars a gram. Here's how it works. When you fuse two kinds of hydrogen, deuterium and tritium, every single reaction kicks out a neutron moving at 14.1 MeV, which is absurdly fast. Fire one into mercury-198 and it knocks another neutron loose on the way through, so two come out and you're left with mercury-197. Give that a few days and it decays into gold-197, the only stable kind of gold there is. The whole trick needs neutrons above about 6 MeV, and regular nuclear reactors barely make those, while fusion makes one every time it fires. Now swap the mercury for radium-226. Same reaction, one neutron in and two out, and you end up with radium-225, which decays into actinium-225. Novartis is building its next prostate cancer drug around actinium-225, and it's so scarce it sells by the millicurie, roughly $1,200 to $2,500 apiece. Price it by the gram and you're into the tens of billions of dollars, for a material so radioactive nobody could safely hold a gram of it anyway. Marathon's team actually put a dollar value on a single fusion neutron in a paper this year. Use it to make electricity and it's worth about $3 × 10⁻²⁰. Gold comes in at $2.6 × 10⁻²⁰, basically a wash. Molybdenum-99, which goes into about 80% of nuclear medicine scans, is worth $2.6 × 10⁻¹⁷ a neutron. Actinium-225 is worth about $10⁻¹⁰, around 3 billion times what the electricity's worth. That's why fusion people should care. Every fusion company you've heard of is chasing electricity, and selling electricity only pays once the plasma puts out something like 10 to 22 times the energy you pump in to heat it. The best result so far, at JET in England, was about a third. Add gold on the side and Marathon's math says you break even around 3x. For molybdenum-99, the number is 0.001x. So a machine that gives back one-thousandth of the energy you feed it can still make money, as long as the neutrons hit the right metal. By their numbers, a 3 megawatt machine running way below breakeven could cover the whole world's supply of molybdenum-99, which today comes from splitting uranium in a handful of research reactors. Today's experimental fusion machines already beat the gain that job needs. Their second post basically says this out loud. Marathon now describes itself as an isotope company for radiopharma. Their chart shows roughly 390 targeted radiotherapy trials. Pluvicto, the prostate cancer drug Novartis launched at $42,500 a dose, did $1.9 billion in sales last year. Gold still matters to them, just later. It's about a $700 billion a year market, and Marathon's paper figures it could soak up around 2 terawatts of fusion capacity, which is roughly the scale where fusion actually moves the grid. So the plan is to sell cancer isotopes out of small machines now and use gold to pay for the giant ones, years before electricity can pay for itself.
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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Introducing ✨Token-Gated Language Models✨ for up to 11x faster prefill by learning to skim context, conditionally forwarding only tokens that matter. Akin to the shift from dense models to MoEs, the next frontier of efficient LLMs is conditional computation over a new scaling axis: context tokens🧵👇
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I could not run to this fast enough
Introducing free People Data for ChatGPT & Claude. Give your AI agents access to 270M+ company & people records in Alexandria. Get 1,000 free credits a month to build prospect lists, find work emails, & research job history with the Firecrawl plugin. firecrawl.dev/alexandria/cat…
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Taking meeting notes is easy; connecting them to your local files in real time is hard. Meet Google AI Edge Foresight, an on-device macOS meeting companion powered by EmbeddingGemma 2. It auto-enhances shorthand notes and searches across local docs, images, and transcripts completely offline.
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Let’s hit this against DuckDB 2.0 reading partitioned S3.
Today we are launching @ObsessionDB, the realtime data warehouse for agents. Your agents are hungry for data. Feeding them is getting expensive. Your data warehouse wasn't built (or priced) for that. We learned this the hard way. We scaled our last company to petabytes on ClickHouse and watched the infra bill go vertical. So we built @ObsessionDB . The ClickHouse engine, rebuilt with compute and storage separated, so your infra scales with your agents instead of your bill. 100x faster and 20x cheaper than Snowflake and Databricks. Already live, processing trillions of rows a day. Agents are just getting started...
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A new Mac Inference engine brings a 48GB M4 Pro MacBook running Qwen3.6-35B to 154 tps. The same benchmark with llama.cpp + MTP, 69 tps. Someone built another Local AI inference engine for Mac. It's called Kapitsa, and instead of trying to support every model, it specializes using🍎 Apple Silicon for either 🧠 Qwen 🟢 NVIDIA Nemotron On a 2024 M4 Pro MacBook Pro with 48GB RAM, running Qwen3.6-35B-A3B at 4-bit: ⚡ Kapitsa + DFlash2 → 154 tps ⚡ oMLX + DFlash2 → 127 tps ⚡ Ollama + MTP → 89 tps ⚡ LM Studio → 86 tps ⚡ MTPLX → 80 tps 🐌 llama.cpp + MTP → 69 tps That's about 2.2× llama.cpp's decode speed in their test. The strategy being, instead of building one generic runtime, Kapitsa hand-optimizes Metal kernels + execution paths around specific architectures, then stacks tweaks like 🚀 DFlash2 speculative decoding 🧠 model-aware caching ⚙️ optimized MoE execution 🔀 concurrent request batching And on an M5 Max 128GB, they report 🚀 Qwen3.6-35B → 281 tps 👁️ Nemotron 3.5 Super-VL 120B → 85 tps ⚠️ These are still developer benchmarks, not independent results. Links in ALT.
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New model Raon-1 mini from Krafton a Korean AI lab! It looks powerful and fast at the same time! Open weights + drafter released!!! It's test time! 🚀
Today, we’re releasing a preview of Raon-1 mini, a 30B MoE LLM with 4B active parameters, trained from scratch by KRAFTON AI. With our efficient architecture, it matches Muse Glimmer on coding/agentic tasks while being much faster and more memory-efficient! 🔥 Plus, natural, human-like conversation. This is just a first look. The final release (and bigger models) are coming soon! 👀 👉 Try it now, free for a limited time: raon-mini.krafton.ai/en 🤗 Open weights + DFlash drafter (Apache 2.0): huggingface.co/KRAFTON/Raon-…
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First, make Postgres really good at search. Then make search really easy to use. That's the thinking behind pg_textsearch, pg_vectorsearch, and now searchgres.js.
Storing vectors is easy. Retrieving the right evidence is harder. Introducing searchgres.js: BM25, semantic + hybrid search in Postgres, with hierarchy, metadata, regex + time filters. Filtering belongs inside retrieval. tsdb.co/searchgres-x
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🚨炸了!上海AI实验室推出新模型Sage-A1.5 !仅35B硬刚Kimi-K3、GLM-5.3、GPT-5.6-sol!同级第一还能反超巨无霸! 35B MoE智能体模型,仅3B激活,主打长程执行与科学探索。 它不仅全面碾压同级A1、Ornith-1.5,在多个指标上还压过Kimi-K3、GLM-5.3、GPT-5.6-sol: 🔹FS-Olympiad 81.0、CMT 80.0、PinchBench 90.0 🔹SWE-Bench Pro 61.2、BrowseComp 82.8、IFBench 81.6 🔹支持子代理并行编排、内置验证协议、开放科学发现 小参数做出大模型级Agent与科学能力,效率与实力双在线。 技术报告:arxiv.org/abs/2610.28578 模型:huggingface.co/InternScience… #上海人工智能实验室 #SageA15 #开源大模型 #AIAgent #科学智能 #中国AI #KimiK3 #GLM53
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