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Microsoft has become Megabloat. Bearly AI (@bearlyai) Tey Bannerman counted up all the products and tools that Microsoft has named “Copilot”. Found 78 of them: “there are now Copilots inside Copilots, Copilots for other Copilots, and a physical Copilot key on your keyboard for summoning them.” — https://nitter.net/bearlyai/status/2040548562053050749#m
→ View original post on X — @pmddomingos, 2026-04-05 02:39 UTC
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Thank you so much for using chat.sakana.ai over the weekend! 🐟 From everyday research to programming rubber-ducking, and even creative uses like "having it write SVG code for an abstract Japanese fish" as shown in the attachment, it seems everyone is using it in various ways. (The image is the rendering result of code written by Namazu 🎨) If you have any helpful answers or interesting use cases like "this response was convenient!" or "this was a fun way to use it!", please let us know with a screenshot via reply or quote repost! ✨ [Translated from EN to English]
→ View original post on X — @sakanaailabs, 2026-04-05 02:23 UTC
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This isn’t an edge case. From anonymized U.S. ChatGPT data, we are seeing: • ~2M weekly messages on health insurance • ~600K weekly messages from people living in “hospital deserts” (30 min drive to nearest hospital) • 7 out of 10 msgs happen outside clinic hours Simon Smith (@_simonsmith) I’ve been critical of OpenAI lately, but for the past three weeks my family has been dealing with a health issue with my dad, and a ChatGPT shared project with live document syncing has been essential to organizing and understanding everything happening. Me, my four siblings, my mom, and my dad have faced an onslaught of information from various doctors and nurses, which we’ve captured in hundreds of text messages and documents and scans and you name it. ChatGPT has helped us collect this information in a single place, make sense of it, and interrogate it to make the most informed decisions possible. Also, credit where due: Claude played an important role as well, by ingesting iMessages and synthesizing summarizes from them to upload to ChatGPT, as well as by extracting text from a bunch of HEIC document scans. I think those of us, like me, excited at AI’s potential get frustrated when we can see issues so clearly, like ChatGPT’s bad design skills, and Claude’s increasing instability and confusing usage consumption. But at times like this I’m reminded of how incredible this technology already is, letting me and my family make sense and act on hundreds of pieces of information, empowering us in the face of a disjointed and fragmented healthcare system. — https://nitter.net/_simonsmith/status/2040539824034115676#m
→ View original post on X — @jeremyphoward, 2026-04-05 01:43 UTC
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Liberate your @openclaw with an open model or local model with these tools from our friend @ClementDelangue and team at @huggingface 🦞 huggingface.co/blog/liberate…
→ View original post on X — @ceobillionaire, 2026-04-05 01:08 UTC
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The most underrated infrastructure play in AI just landed — and almost nobody is talking about it. @Microsoft quietly released three new MAI models this week, and one of them changes the game for voice-first products. MAI-Transcribe-1 delivers state-of-the-art speech-to-text across 25 languages at 2.5x the speed of any existing Azure offering. Why does this matter more than another chatbot upgrade? Because the next billion AI users will not type. They will speak. In Lagos, in Jakarta, in Sao Paulo — the default interface is voice. And whoever owns the transcription layer owns the entry point to every downstream agent, workflow, and decision. This is the same pattern we saw with cloud storage a decade ago. Nobody built a business on S3. But almost every important business ran on top of it. Transcription is becoming the S3 of agentic AI — invisible, essential, and a winner-take-most market. @SatyaNadella is not competing with @OpenAI on vibes. He is competing on plumbing. And plumbing tends to win. If you are building anything voice-adjacent — customer support, health intake, field operations, education — this week's release should be on your radar. Daily rec: @benedictevans on infrastructure economics in AI. Always worth reading twice.
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"An Abstract Fish in Japan" (Pure SVG code generated by Sakana Chat) [Translated from EN to English]
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Pre-training: imitate humans Post-training: please humans Inference: beat humans
→ View original post on X — @pmddomingos, 2026-04-05 00:06 UTC
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Just spent 10 minutes playing the ARC-AGI-3 games and i genuinely cannot get over it. You figure out the rules yourself in like 2-3 minutes. no instructions. just vibes. GPT-5, Gemini 3 and Claude score below 1% on these. Try it yourself: arcprize.org/arc-agi/3
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It has about been a tool to enable and open to more but most use this as the main dish. That’s why it’s dead
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Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet.
— Andrej Karpathy (@karpathy) 4 avril 2026
I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something:
1. Explicit. The memory artifact… https://t.co/omsJsy8MXq
Farzapedia, personal wikipedia of Farza, good example following my Wiki LLM tweet. I really like this approach to personalization in a number of ways, compared to "status quo" of an AI that allegedly gets better the more you use it or something: 1. Explicit. The memory artifact is explicit and navigable (the wiki), you can see exactly what the AI does and does not know and you can inspect and manage this artifact, even if you don't do the direct text writing (the LLM does). The knowledge of you is not implicit and unknown, it's explicit and viewable. 2. Yours. Your data is yours, on your local computer, it's not in some particular AI provider's system without the ability to extract it. You're in control of your information. 3. File over app. The memory here is a simple collection of files in universal formats (images, markdown). This means the data is interoperable: you can use a very large collection of tools/CLIs or whatever you want over this information because it's just files. The agents can apply the entire Unix toolkit over them. They can natively read and understand them. Any kind of data can be imported into files as input, and any kind of interface can be used to view them as the output. E.g. you can use Obsidian to view them or vibe code something of your own. Search "File over app" for an article on this philosophy. 4. BYOAI. You can use whatever AI you want to "plug into" this information – Claude, Codex, OpenCode, whatever. You can even think about taking an open source AI and finetuning it on your wiki – in principle, this AI could "know" you in its weights, not just attend over your data. So this approach to personalization puts *you* in full control. The data is yours. In Universal formats. Explicit and inspectable. Use whatever AI you want over it, keep the AI companies on their toes! 🙂 Certainly this is not the simplest way to get an AI to know you – it does require you to manage file directories and so on, but agents also make it quite simple and they can help you a lot. I imagine a number of products might come out to make this all easier, but imo "agent proficiency" is a CORE SKILL of the 21st century. These are extremely powerful tools – they speak English and they do all the computer stuff for you. Try this opportunity to play with one. Farza 🇵🇰🇺🇸 (@FarzaTV) This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase! — https://nitter.net/FarzaTV/status/2040563939797504467#m