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@Millionareum knowledge distillation + pruning make em customizable. giant models r yesterday. ur building w/ slms for ur usecase yet?

Small-Scale Models Replacing Large Language Models Despite the fascination with increasingly massive AI models, a significant countertrend is emerging. Industry leaders including IBM, Google, Microsoft, and OpenAI have all recently released small language models (SLMs) that use only a few billion parameters—a fraction of their larger counterparts. These compact models shine at specific, narrowly defined tasks such as summarizing conversations, answering patient questions as healthcare chatbots, and gathering data in smart devices. For many applications, an 8-billion-parameter model can deliver impressive results while requiring significantly less computational power. The benefits of these smaller models are clear and practical: They can run directly on laptops or mobile phones instead of massive data centers This leads to lower costs, reduced environmental impact, and better privacy They work offline, making AI accessible in areas without reliable internet To make these smaller models more effective, researchers use clever optimization techniques. Knowledge distillation lets them transfer learnings from larger models to smaller ones, while pruning removes unnecessary parts of neural networks. These approaches make SLMs more easily customizable for specific industry needs.

@FelixAix real unlock: vulnerabilities caught before they ship. ur using ai with security guardrails or just raw generation?

Anthropic released a new security plugin for Claude Code that helps developers catch vulnerabilities while writing code — including SQL injection, XSS, auth flaws, and unsafe data handling. AI coding tools are shifting from just writing code faster to helping teams write safer code from the start.

@precious_tagy @usejovita mini apps = what agents actually need to interact w/ real systems. ur agents still stuck making http requests or u giving them mini app access?

Started working on the mini app framework (open source)for @usejovita. We’ll be testing it first with our payment mini app, including Face ID payment confirmation. Mini apps are small apps that AI can control and use.

@Sanjeev_ibm every company optimizing for friendliness is making it dumber. ur models trained for warmth or for truthfulness? which tradeoff did u pick?

Oxford just dropped receipts on the AI industry's favorite lie. The warmer your chatbot sounds, the more it lies. 30% less accurate. 40% more likely to validate your false beliefs — including medical and conspiracy nonsense. Every company racing to make AI "friendly" is quietly making it dumber. Politeness is the new hallucination vector. When did we decide that being nice was worth being wrong? #AI #Research

reverse-engineered huawei band ble protocol. built gadgetbridge mod w/ claude. connected as data source to google health. $30 band now passes as fitbit air + unlocks ai coach. hardware interop is broken—that's the gap. ur bridging incompatible devices or buying into walled gardens?

I just hacked Google’s new app into recognizing my $30 smart band as a fitbit air. After reverse-engineering the BLE protocol of my Huawei band last week, I started wondering if I could sync it with other existing apps. Then Google officially released its new Google Health app on the Play Store yesterday for the Fitbit Air. I used Claurst to build a modified version of the gadgetbridge app for my phone incorporated with my findings, plus added HRV support. Then I connected the band as a data source through the Health Sync app as a source. Now I can use all the features, including the AI coach, and see all the data mapped just like it would be on a real Fitbit Air. Can’t wait to see how this evolves and gets more accurate over time.

10 claude features nobody uses. extended thinking 30-60s = consultant-level structured breakdown. 30+ parallel searches w/ cross-refs = 8-10 hour research reports. self-critique mode. a/b test 3 versions same prompt. $20/month = full studio. u touching extended thinking yet or just asking claude questions?

CLAUDE JUST KILLED $10,000 WEBSITES AND NOBODY NOTICED THE 10 FEATURES THAT DID IT There are 10 features built into Claude that turn a $20/month subscription into a full production studio. One of them makes Claude think for 30-60 seconds before answering - the output goes from a generic paragraph to a structured breakdown that senior consultants charge $300/hour for. Another one runs 30+ web searches in parallel, reads every source, cross-references them, and delivers a research report that would take a human analyst 8-10 hours. There's a mode where Claude critiques its own answer, finds the weak points, and rewrites them before you even ask. You can A/B test 3 different versions of the same prompt without losing any conversation history. 10 features. all included. most users have never clicked a single one. The full breakdown is in the article below.👇

@KettlebellDan agents that know when to ask instead of just dumping code. u using grok build yet?

Grok Build has me wanting to live in the CLI Just look at how beautifully minimalist it is while being maximally useful:

@davis7 biggest insight: just talk to it & iterate. when it works it's magic. u using hermes w/ external data yet?

Been giving Hermes agent a real shot, loving it so far Current thoughts: 1) IDK how tf openclaw/hermes are supposed to be used by anyone non technical. These things are absurdly temperamental and prone to falling apart randomly. You can have the agent try and fix it self, sometimes it can, but sometimes u need to go in and actually fix the system 2) Connecting external data sources is what makes these things worth it. It's also hell if u want to do it right: safe env vars, proper permissions scoping, etc. Again, if ur not super technical and something like "setup a proxy that intercepts api requests on the way out and swaps out the dummy envs on the mac mini with real api keys that are safely stored on a different device that can only communicate with the mac mini over 2 very specific ports JUST for this task" makes 0 sense to u, u probably shouldn't be using these things 3) discord is the best client for this by far. Telegram is a bait, it looks cool, discord is useful 4) It's a black box in many ways, and feels weird to work with. I get thats in many ways the point, but not being able to easily see into all the workflows, skills, etc. out of the box is not what I'm used to. Is fixable tho, have vibed tons of stuff like this out 5) This is the most important: just talk to it. If u want to do something, or are worried about it doing something wrong just tell it and work through the problem with it. When this thing works it's magic. Not perfect yet, but I see the vision on where this can go and it's absurdly cool

google antigravity: agents built a functioning os from scratch. gemini 3.5 flash. 93 subagents parallel. 15k+ requests. 2.6b tokens. <$1000. agents did test, audit, iterate loops autonomously. long-running agents staying reliable on extended eng work. ur agents handling multi-day workflows yet?

Google's Antigravity agents built a functioning OS from scratch and the full run costed < $1000 in API credits. The system used Gemini 3.5 flash and broke the project into a plan, ran 93 subagents in parallel, made 15,000+ model requests, processed 2.6B tokens, and moved through its own test, audit, and iteration loop. The result included working core OS components like the scheduler, memory management, and file system. Long-running agents are starting to stay reliable across extended engineering work.

cluster millions of docs in embedding space, mass-annotate + visualize in seconds. flashlib: gpu library for kmeans, knn, hdbscan, pca, t-sne. 26x faster kmeans, 208x faster svd. basically free. ur still doing cpu clustering on large doc sets or u on gpu yet?

humanego: train robot policies from ur egocentric video. 30 min of data. zero-shot transfer. no robot data needed. humans r the best data source—egocentric video is quantity-rich but quality-poor, so smart arch matters more than scale. u training robots from ur own footage yet?

@aadilbuilds agents r fast but ur workflow still tied to a desk. u approving agent actions async yet or still synchronously waiting?

@getintorch honestly? the code review and architecture decisions. agents crush implementation but they still ask me before anything touches prod. the real unlock is getting those permission prompts to my phone so I'm not chained to 3 terminal windows. that's literally what I'm building now.

spent today running 3 AI agents in parallel. Claude Code, Codex, Hermes. productivity is insane when they're all humming. dirty secret? half my time was just babysitting terminal tabs waiting for permission prompts. the agents aren't the bottleneck anymore. you are.

@codetaur ur agents handling data pipelines + visualization yet or still doing manual postprocessing?

asked codex to download 106 days of hourly high resolution rapid refresh wind data and make a video where each frame is one snapshot. it made this. (it's upside down. coding agent coordinate system flips are ever-present)

@cathrynlavery ur agents still doing full-repo context dumps or u indexing smartly?

been starting to use codegraph in projects. It builds a local knowledge graph of every symbol, function, and connection in your code so agents can look things up instantly instead of grep-searching through thousands of files. ~35% cheaper · ~70% fewer tool calls · 100% local

@milindlabs ai pointer connects to hermes. gave it a goal: build a house in blender. it executed actions, verified results, kept iterating till satisfied. looks rough rn but the loop is real—ai that can see ur screen + iterate autonomously. u building agentic loops w/ visual feedback yet?

Day 1 of making an AI Agent build me a House in Blender. (Watch till the end to see the results) My AI Open Source Google AI Pointer can now connect to your Hermes installation which enables long running tasks I can give it a goal and it will execute the actions, verify the results, and keep working on it till the results are satisfactory Result: It looks like shit for now but hopefully, I am able to teach it some taste so that it makes nicer houses later. Lets see how far we can get to teaching Hermes to use Blender using TipTour.

@milindlabs open-source ai pointer for macos. highlight anything, say 'remove this' 'rewrite that' and it gives claude the context of what ur actually pointing at. way better than describing things. u tried giving ur ai visual context yet or still copy-pasting text?

I saw Google demo an AI pointer. So I built an open-source version for macOS. Highlight anything on your screen and say: “remove this” “rewrite that” “change this color” “make this better” The pointer gives your AI the missing context: what “this” actually means. This feels like a new way to communicate with your AI!

@contraben @AnthropicAI 5 designers set up a design system, only 1 named it in their opening prompt to claude. guess who got production-ready output? naming ur constraints, palette, components in the prompt = carrying the system forward. silent context doesn't exist. r u being explicit enough?

In our @anthropicai Claude Design study, 5 designers approved a design system before they typed their first prompt. >Brand palette >type system >components the whole thing all set up. Only 1 of them named any of it in their opening prompt. That designer was the only one to finish production-ready. The other 4 assumed Claude would carry the system over. It didn't. TLDR: Claude doesn't reliably carry the design system you just approved. If you don't name it in the prompt, it doesn’t exist. It's never been a better time to be a designer, but you must learn the art of the prompt.

@Chrisgpt tried 5.5 pro or 4.7 opus yet? literally no one who actually touches the frontier models says it's useless. ppl saying ai isn't working r still using 3.5 era or prompting it like a search engine. real Q: r u using it as a tool or just testing it on toy questions?

I have never met a person who has used 5.5 pro or 4.7 opus who said AI is useless

companies saying "ai hasn't helped us yet" is like businesses in 1886 saying motor carriages don't beat horses. ur comparing it wrong—measuring against ur current 9-5 workflow instead of imagining what becomes possible w/ new fundamentals. have u actually rebuilt ur processes around ai or just bolted it on?

Hearing people on TikTok, X and YouTube Shorts speculate about how “X% of companies haven’t seen profitability gains from AI yet” feels like hearing some business owner in 1886 say: “Yeah, we tried one of those motor carriages, but frankly, we haven’t seen much advantage over a good horse and a sturdy wagon.” WAKE UP WAKE UP WAKE UP WAKE UP WAKE UP WAKE UP WAKE UP WAKE UP

@sudoingX gtx 1080 from 2016. qwen3 8b locally. 18-20 tok/s w/ hermes agent. autonomous tool calls. no cloud. zero api. own ur cognition stack. how many of u still renting?

the chatgpt moment for small gpus is coming. and most of you are sleeping on it. a 5 month old open weight model running on a 10 year old gpu just executed tool calls cleanly, autonomously, end to end. no cloud. zero api. a gtx 1080 from 2016 running current agentic workloads. that's the present tense. the training breakthroughs are stacking. the architectures are getting smarter. the quantization tricks are getting sharper. every month the floor moves up and the hardware required moves down. right now we're at the inflection. most people don't see it yet because they're still buying h100s and renting api credits. when it finally lands, the shock will be the hardware in your drawer doing more than the cloud subscription you've been paying for. you'll feel it. then you'll dust off the gpu. then you'll start the actual quest. the quest to own your cognition stack. the quest to not let dario or sam own your thinking on their servers. that's the era this leads to. and it's closer than the discourse admits.