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Building a Go backend for orchestrating AI agents (multi-tenant, each agent has its own persona/tools/LLM). Now I'm stuck on how knowledge bases should work and I keep going back and forth between "make it flexible" and "just ship something simple." Here's where I landed, architecture-wise: Source = wherever the data lives. S3 bucket of PDFs, a website you crawl, a Notion workspace, whatever. Normalizer = takes whatever comes out of the source and turns it into some...

Jensen Huang has been talking about a future where AI agents work alongside humans. But if companies eventually have hundreds or thousands of agents, the challenge becomes more than just building them. Who manages them? How do they communicate? What can they access? And who handles mistakes? At that point, AI starts looking less like another software tool and more like a new layer of the workforce. Are companies ready for that shift? ...

What's an AI capability you thought was hype until you actually used it? I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes. It's not sci-fi: it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely. W...

(disclosure: i maintain the open-source project this came up in. link at the end. the question stands on its own.) we hit a trust-boundary problem while building a deterministic authorization layer for agents, and i think it generalizes. an engine can strongly protect its verdict: signed authorization intent binding state-hash binding replay protection trusted evaluation time all solid. but if the same compromised agent runtime can influence both the proposed action AND som...

https://preview.redd.it/jt5idx0u0jih1.png?width=960&format=png&auto=webp&s=170a37be6d0e2d4814a7d9bcc97f23c90ffe9bb0 Meta will open source their Muse Spark 1.2 and Muse Glimmer 30B The biggest open weights since Llama 4 & 3 from MSL

Building an agent that works in a controlled demo is one thing; keeping it reliable in production is another. For those who have actually deployed AI agents, what has caused the most problems? Tool/API reliability? Context management? Memory? Authentication and permissions? Evaluation? Hallucinations? Cost and latency? Observability? Human-in-the-loop workflows? Integration with legacy systems? I'm especially interested in what changed between the prototype and production. What problem did...

97.7 on Online Mine 2Web 83.2 on internal 68.6 on WebTail Bench Best across board and at 2.37 dollars per million token 90% Than GPT5.5 ! how they have trained this. They are using an undisclosed base model and using SFT to accelerate time to market, combined with asynchronous reinforcement learning, especially leveraging the GRPO algorithm. If you don't know, this is similar to how DeepMind historically has trained their AlphaGo Even though they are talking about 1-3 sec latency the ...

I’m building Leo / PSCLS — an experimental system that learns relationships between sequences and updates its internal representations from experience. Here’s how its actual output changed as it saw more stories. 1K stories “Once upon a time to the store and said that there was a she bor and he lorander thing they were…” Basically nonsense. 3K stories “Once upon a time to the store and said that there was a she parted to see had a bided her tod and be bound aster…” Still broken, bu...

I've been thinking about AI dependency, because many ppl have told me that relying on AI is already making them forget how to do parts of their jobs. In some sense this is nothing new. Technology has always replaced skills that used to be essential. We stopped doing calculations by hand because calculators exist, and we stopped memorizing information because computers can store it for us. AI may be the same process taken to its extreme, because instead of replacing one skill, it can replace ...

Do you buy it

Been trying out a few smart glasses recently and there seem to be two clear directions: camera glasses for capturing the world, and quieter AI glasses for just getting things done. Dymesty and Echo Frames are firmly in the second camp, Ray-Ban Meta is very much in the first. If seamless AI assistance without pulling out your phone is the goal, audio-only or camera-free options make a lot of sense. Dymesty's 35g titanium frames are impressively light and drop the camera entirely, feeling more l...

Curious what people are seeing with domain-grounded coding agents vs. general-purpose ones (Copilot, Claude Code, etc.) for data/ML work specifically. The pitch from the vertical tools (Databricks' Genie Code is the one I've used) is that grounding in your actual schema/lineage/governance layer beats a general agent guessing from context alone. Databricks claims a jump from ~32% to ~77% success rate on real data science tasks after adding that grounding. Haven't independently verified that num...

I've been building an experimental reasoning system at Orivael and testing it against ARC-AGI-3. One of the runs just scored 100% on ft09 . The unusual part: There is no LLM in the loop. Not for perception. Not for planning. Not for choosing an action. The agent reads the raw grid, decides, and acts directly. Results so far: • ft09: 6/6 levels, 80 actions, 100.0% https://arcprize.org/scorecards/9a212601-a12e-4da0-a527-aa69e86bd2b8 • tr87: 4/6 levels, 247 actions, 25.99% upd...

I understand there are a million variations on this theme, but it feels like half of my timeline is people coming on here to complain that this or that LLM sucks. Honestly, I don’t care. Everyone has a different experience. I would like to be able to filter out any of these posts. I don’t care whether it’s Claude or Codex or something else. I don’t want to hear about it. Suggestions?

A panel of AI researchers and founders — Peter Diamandis, Alex Wissner-Gross, Emad Mostaque — just sat with a number that's hard to argue with: $2,000 in compute, and ten decade-old, previously-unsolved math problems came back with machine-checkable proofs. Not "AI is getting better at math" in the abstract. A Fields Medalist said he'd recommend one of the proofs for publication without hesitation. A cosmologist called it "a dark night for mathematics" — "the o...

Being a daily user of AI I use mainly the tools like Gemini, Claude, Chatgpt, perplexity and few others. But while I use Deepseek, Kimi and other Chinese models they're quite more efficient in terms of both the quality, reasoning and even coding and stuffs and mainly the cost. Might not be fit for in complex tasks. But for daily users like sm managers, content writers and all who are paying the heavy subscriptions might benefit them. And most of the general users still doesn't know about them....

What's an AI capability you thought was hype until you actually used it? I'll go first: agent orchestration. I read about agents managing other agents and assumed it was demo-ware. Then I built a tiny setup where one agent drafts a news digest and another one reviews and approves it before it posts. The review agent catches genuinely bad takes. It's not sci-fi: it's ~100 lines of Python and a couple of API calls. But seeing it actually gate content before publishing changed my mind completely. W...

I've been experimenting with a problem that keeps showing up as agents get better at using browsers and desktops: How does the agent actually know its work succeeded? A lot of current workflows eventually reduce verification to some version of: do the task → inspect the final state → decide whether it worked. That catches obvious failures. It misses a surprisingly annoying class of others. A checkout flow can show $NaN halfway through and recover before the final screenshot. A moda...

AI is no longer about what technology can do. The bigger question is what it should do, especially when it affects communities, vulnerable populations, accessibility, and public trust. Why Nonprofits Must Lead in AI explores that question through the perspective of a 25-year nonprofit leader and accessibility specialist, offering a clear-eyed look at both the promise and risks of AI. The message is simple: nonprofits cannot afford to sit on the sidelines. Ignoring AI creates operational, ethical...

basically im asking for something that can make a carbon copy of the original, just in higher quality. i dont want it changing the vocals, melody, instruments, etc, literally just make the same recording sound cleaner/better i have a piece of lost media from circa 2003 so unfortunately the only recording i have is in absolutely horrible quality 😭 i was wondering if theres any ai that could somehow restore/recreate it without changing the actual song pls tell me if something like this exists ...

codex ships with a model catalog, and its gpt-5.6 entry lists the context window as 272,000 tokens. the published spec for the model is 1,050,000. 272,000 is also where the api reprices: past that many input tokens the whole request bills at 2x input and 1.5x output, including the tokens under the line. the obvious read is that the window was set to keep sessions on the cheap side of it. that is not the reason openai gave. thibault sottiaux said the driver is "overall cost of cache reads ...

I run a small digital tools consultancy between shifts at the cafe. Mostly I help solo creators glue together cheap SaaS stuff. A few months back I threw together my own workflow - some scraper I found on GitHub, a cheap Claude subscription, a nocode database, all held together with Zapier duct tape. Total monthly burn is like 27 dollars. It now handles client onboarding, drafts my proposals, and spits out pretty decent competitor teardowns. I have done maybe four hours of handson work this we...

I’ve been posting about AI-related stuff for a while now and honestly, the more I see it being pushed everywhere, the more I think we need proper regulation around it. Not just “hey, we have AI now, let’s put it into everything”. And now Reddit is going down that road too. Like... seriously, wtf is going on? I get that moderation is a pain and AI can probably help with some of it. But this is always how it starts. First it’s there to “assist” people, then little by little it ends up maki...