by MicroWagie ·
I'm not really skeptical of AI agents and AI in general, it really is a force multiplier and I have used it a lot to learn and do weeks work in days. At the moment my setup as a wagie @ MAG7 is cursor and claude code. How are people using Agents in their work and is it actually useful?
Have you guys seen any cool enough projects or blogs which explicate the extent of the agents usefulness? What the fuck is an agent anyway
by UmYeahNo ·
Curious to know your experience running local LLM's with a well spec'ed out M3 Ultra or M4 Pro Mac Studio. I don't see a lot of discussion on the Mac Studio for Local LLMs but it seems like you could put big models in memory with the shared VRAM. I assume that the token generation would be slow, but you might get higher quality results because you can put larger models in memory.
by prateekdalal ·
Over the last year, we’ve been experimenting with LLMs inside enterprise systems.
What keeps surfacing is a fundamental mismatch: LLMs are probabilistic and non-deterministic, while enterprises are built on predictability, auditability, and accountability.
Most current approaches try to “tame” LLMs with prompts, retries, or heuristics. That works for demos, but starts breaking down when you need explainability, policy enforcement, or post-incident accountability.
We’ve found that treating LLMs as suggestion engines rather than decision makers changes the architecture completely. The actual execution needs to live in a deterministic control layer that can enforce rules, log decisions, and fail safely.
Curious how others here are handling this gap between probabilistic AI and deterministic enterprise systems. Are you seeing similar issues in production?
by nanocat ·
Since it seems like 80% of HN nowadays is focussed on the AI industry, I’m on the search for a good tech forum that focuses on the rest. Can you post your favourite non-AI-obsessed forum?
by jlmcgraw ·
I’m looking for some good, specific ideas on small ways to have a positive impact on the world on a daily basis.
What do you consider to be the highest return-on-efforts ways to make the world a better place for as many people as possible?
by Invictus0 ·
I understand Llama 4 was a disappointment, but what's happened at Meta since then? Their API is still waitlist-only 10 months on.
by whoishiring ·
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by netfortius ·
Title basically tries to capture the gist of the question. I have been asked (volunteer) to assist in the project of migration from a proprietary, more costly CRM solution, to an Odoo Community "product", to be architected, configured, deployed in a cloud service and operated by a specialized partner. My specialization is in infrastructure (architecture, ops and security), so I could certainly validate mapping the apps functionality into the right components, but I have zero knowledge on how good the CRM part is, and - crucially - how to keep its possible need for customization in time and operations cost low, if internal org volunteers have no technical skills. I am concerned about the integrator attempt to get the foot in the door with an acceptable one time cost, then slowly ramp up the price, if this solution requires a lot of babysitting.
Does anyone have any experience with this Odoo Community CRM product and model, to share some gotchas, in the light of the above described attempt to use? Users max 300. The hope is to also have the CRM integrate with needed office products (doc, spreadsheet, email, etc.)
by PranoyP ·
I built a system where two LLM agents co-evolve: one invents regex problems, the other learns to solve them. The generator analyzes the solver's failures to create challenges at the edge of its abilities.
The result: autonomous discovery of a curriculum from simple patterns to complex regex, with a quality-diversity archive ensuring broad exploration.
Blog: https://pranoy-panda.github.io/2025/07/30/3rd.html
Code: https://github.com/pranoy-panda/open-ended-discovery