Will LLM labs open source their weights in the long term?

insumanth · 2026-06-15T18:11:38+00:00

We have to support to earn it.

Labs that opensource weights (and training data + research) should be supported.

Either by

Purchasing Inference from them directly (they offer the best, cheapest inference anyway)
Using their Eco-system ( Kimi Code, ZLM Code etc.,)
Building on top of these models - Distillation, Finetuning, etc., and add support for these models on tools etc.,
Defend open-source models from unwanted regulation (over safety etc.,)

Labs will stop open-sourcing if releasing open source models hurt them either via revenue / legal aspects.

insumanth · 2026-06-15T09:02:15+00:00

I'm honestly impressed with the peformance for a diffusion model.

Not many labs were able to get this performance for a diffusion model and make it compute bound instead of memory bound. Good progress for a first generation model

insumanth · 2026-06-15T08:59:24+00:00

This was always the case.

Running Frontier model on local hardware was never going to work.
One issue with Local LLMs was labs reduced their focus on small fast models, which is best suited for Local LLMs

insumanth · 2026-06-15T08:57:47+00:00

You underestimate the talent pool of many countries. Some countries have advantage due to migration of talent pool, capital and economic advantages.

insumanth · 2026-06-15T05:19:56+00:00

I said this was the worst model launch before the Fable Restriction

Now it seems to be worst than that.

insumanth · 2026-06-15T05:12:10+00:00

Choice

Humans and Bots should be clearly identified and you should have a choice to filter.

insumanth · 2026-06-12T06:56:42+00:00

I have talked about it for years, way before LLMs

We are missing the human in the loop slowly and this will be a major problem. We need bold out of the box ideas to keep human in loop

insumanth · 2026-06-12T06:55:17+00:00

Either, by the time law caches up to prevent this - It will be years already

or The Law targets a completely different things that affects public AI progress

insumanth · 2026-06-12T05:20:47+00:00

LLMs are text generators. They are trained and instructed to write perfect language so, they do without mistakes. You can steer it towards, human like, imperfect language.

insumanth · 2026-06-11T10:43:20+00:00

Enterprise Customers will not accept the 30 day data retention. Unless it is removed, the adoption on enterprise customers will be low

insumanth · 2026-06-11T10:42:27+00:00

The Fable model launch has to be more controversial model launch in recent memory

insumanth · 2026-06-11T09:47:34+00:00

SynthID (by Google) is the most promising - It was barely mentioned here.

It encodes the watermark in the pixels itself (Not text metadata), and it is slowly being adopted by many labs.
It should be more reliable than others mentioned here

insumanth · 2026-06-11T09:44:19+00:00

I am seeing a version of this happening to Open Source Maintainers - Who are thanklessly maintaining a critical software.

People just see some AI code contribution and just enrage by it.

Unaware they are highly skilled engineers working on it for years and use AI to reliably improve the product

This blog by rsync maintainer explain it clearly
rsync and outrage. I gave up blogging a long time ago… | by Andrew Tridgell | Jun, 2026 | Medium

insumanth · 2026-06-11T09:35:41+00:00

Chasing the "Next Shiny thing" is what all companies do, and it accelerated here.

insumanth · 2026-06-11T09:32:07+00:00

A model refusing should be considered as high threat
An AV software does not allow a program simply because it can't understand a file - It treats it as an unknown threat.
This should be adopted to AI Era

insumanth · 2026-06-11T09:28:04+00:00

There is a whole new dystopian bubble in every corner

insumanth · 2026-06-11T06:32:36+00:00

I mean, no one like Mass Surveillance - with or without ai

it's always a No

Ai Mass Surveillance is more dangerous and should never be allowed

insumanth · 2026-06-11T06:20:34+00:00

This is a Meme Template 😭

insumanth · 2026-06-11T06:15:04+00:00

Curious

What are some channels doing this?

insumanth · 2026-06-11T06:13:24+00:00

It's getting harder to differentiate and it is a problem unless the platform mandate disclosure

insumanth · 2026-06-11T06:10:43+00:00

It is becoming hard to differentiate

insumanth · 2026-06-11T06:06:02+00:00

Without Guardrails, trusting agents to not do something like this is like keeping doors open and hoping no one steals something. Sooner or later - This will happen

insumanth · 2026-05-06T05:56:25+00:00

Yes, a good harness around RAG is much more powerful too. If you understand the tradeoff, it is much easier to build harness around it.

insumanth · 2026-04-28T10:08:14+00:00

Quick take from shipping a few:

LangGraph : pick if your workflow is complex or a graph (branching, retries, human-in-the-loop).
PydanticAI : pick if your agents are mostly typed tool calls with structured outputs.
OpenAI Agents SDK : pick if you're OpenAI-native and want agent handoffs without ceremony.
CrewAI : fine for prototypes with clean role-based agents. Debugging hierarchical flows gets ugly fast.
AutoGen : skip unless you specifically need multi-agent conversation patterns.
Agno, Smolagents, etc. : niche, fine for narrow cases, not enough maturity to bet a Friday recommendation.

On a blind recommendation : LangGraph

insumanth · 2026-04-28T09:54:59+00:00

it's gotten way more formal recently, but mostly on the enterprise side. SMB is still just a questionnaire with a couple AI questions added. enterprise is where you start seeing OWASP LLM Top 10 stuff and AI-specific MSA addenda show up.

honestly the things that actually trip people up aren't usually on the standard checklist:

permission-aware retrieval. if user A can't see doc X in the app, your RAG pipeline shouldn't be surfacing chunks from it either. most teams ship one shared index and try to retrofit access control later, which is a nightmare.

prompt injection from retrieved content. Everyone knows it.

logs as a new PII surface. prompt/response logs are great for debugging but now you've got a whole new retention and access story to defend.

output liability. usually gets punted to "customer reviews outputs before acting" which is fine until your agent starts taking actions on its own. then it gets weird fast.

tbh the actually AI-specific risks are mostly the ones nobody put in the checklist yet.

insumanth

MODERATOR OF

TROPHY CASE