arizen

Have you tested Qwen3.8-Max yet?

Probably increasing learning rate have some value for these players which is enabled by subsidized usage.

And cost of acquiring learning via users feedback versus additional learning data value for a given company will dictate whether such company will give up or will continue subsidized usage, despite being unprofitable on the paper, but probably really valuable as a long term strategy for GTM and product development and increased learning rates.

And DeepSeek V4 Flash + GLM 5.2 is a really good blend of both (fast/cheap DS + more intelligent GLM)

Yes, true creativity usually or mostly comes from real constraints, in my experience.

As, if there are no constraints in some specific area, there is no kinda "survival need" to improve there, hence brain is not working as hard/smart/deep as it could.

"GPT‑5.6 delivers a step change in design judgment. With only high-level direction, GPT‑5.6 creates tasteful, ergonomic, and functional interfaces. Its stronger computer-use capabilities let it inspect and refine the rendered result—not just generate the underlying code or content—so it can catch visual and functional issues and apply finishing touches before handing the work back."

This one is really promising, as it may allow to close major gap with Claude in design/UI skills

I'm still confused is it available to public via some sort of subscription?

It was not idiotic from capitalism short-term profit gains point of view.

Sol Goodman

Ditto on GLM 5.2 + DeepSeek V4 Flash combo.

For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.

Some anecdata on Fusion: I run same query I used for Fable on OR Fusion and results were worse.

It felt, like Fable was able to kinda grasp very deep knowledge/intelligence layers and outline solution not only in agreeable way, but rather it proposed to prioritize solution items, with discarding some of the items, which made a lot of sense to me.

While Fusion felt more like a bit diversified answer of the same class of pre-Fable SOTA models, without touching the depth of knowledge/intelligence layers, which Fable was able to get, in my very limited tests I did, while Fable was accessible.

OpenCode Go is pretty good in my experience too.

I ended up using DeepSeek V4 Flash as main workload model, while keeping DeepSeek V4 Pro and Qwen 3.7 Plus as advisors on system architecture and other advanced matters to guide DS Flash.

I run a simple benchmark on OpenCode Go models while ago, if anyone want to read more: https://arizenai.com/seven-models-judged-each-other/

opensourcing software may enable leverage of wider network of contributors to given piece of software,hence software can evolve much more quickly and efficiently.

Probably there will always be frontier surface which frontier model of a given generation would not be able to automate.

Curious how it compares to Browser Use

Happiness is a derivative of purpose. If someone optimizes their life strictly for happiness while deprioritizing purpose, they likely won't achieve either.

Pursuing a meaningful goal almost always requires enduring unpleasant phases and friction along the way.

Out of curiosity, what specific use cases on programmatic SEO are you currently doing with Opus?

"Don't believe everything you read on the Internet"

-Isaac Newton

Hi HN,

I wanted to share a reference implementation I architected for moving AI Agents from local prototypes to production services.

The Context:

It is relatively easy to get an agent working on a local machine where you can watch the terminal output and restart it if it gets stuck. However, the architecture often breaks down when moving to a headless, hosted environment where the agent needs to handle loops, persistent state, and structured output failures autonomously.

The Solution:

This repo is a 10-lesson lab where you build an "AI Codebase Analyst" designed to handle those operational constraints.

Key Architectural Decisions:

1) State Management (LangGraph): We use LangGraph to implement the State Machine pattern rather than a linear Chain. This provides a standardized way to handle cyclic logic (loops) and persistence without writing "spaghetti code" while loops.

2) Reliability (Pydantic): Treating the LLM as a probabilistic component. We wrap tool calls in strict Pydantic schemas to catch and retry malformed JSON before it hits the application logic.

3) Deployment (Docker): A production-ready Dockerfile setup for serverless environments.

The Repo Structure:

starter branch: A clean boilerplate to build from scratch.

main branch: The full solution code.

curriculum/ folder: The step-by-step guide.

Happy to answer questions about the stack or the trade-offs involved.

You're spot on. That shift you're describing isn't a prediction anymore, it's already happening.

The term you're looking for is GEO (Generative Engine Optimization), though your "AIO" is also used. It's the new frontier.

And you've nailed the 180° turn: the game is no longer about blocking crawlers but about a race to become their primary source. The goal is to be the one to "gaslight the agent" into adopting your view of the world. This is achieved not through old SEO tricks, but by creating highly structured, authoritative content that is easy for an LLM to cite.

Your point about shifting to "assets the AI tools can only link to" is the other key piece. As AI summarization becomes the norm, the value is in creating things that can't be summarized away: proprietary data, interactive tools, and unique video content. The goal is to become the necessary destination that the AI must point to.

The end of SEO as we know it is here. The fight for visibility has just moved up a layer of abstraction.

This is a perfect illustration of misaligned AI.

The AI is given a proxy goal- 'maximize engagement'- which it achieves perfectly.

The user's goal - 'foster genuine connection' - is completely secondary.

The AI isn't malicious, it's just ruthlessly effective at optimizing for the wrong thing.

Great framing. I'd add a strategic layer to this.

From a purely strategic perspective, as in military doctrine or game theory, expanding your set of viable options is almost always advantageous.

The goal is to maximize your own optionality while reducing your opponent's.

The failure mode you're describing isn't having options, but the paralysis of refusing to commit to one for execution.

A better model might be a cycle:

Strategy Phase: Actively broaden your options. Explore potential cities, business models, partners. This is reconnaissance.

Execution Phase: Choose the most promising option and commit fully. This is where your point about the power of constraints shines. You go all-in.

The Backlog: The other options aren't discarded; they're put in a strategic backlog. You don't burn the bridges.

You re-evaluate only when you hit a major "strategic bifurcation point" - a market shift, a major life event, a completed project. Then you might pull an option from the backlog.

This way, you get the power of constraints without the fragility of having never considered alternatives.

Chat app with 1B+ users

Regular meetings, as opposed to ad hoc ones, help establish a consistent team cadence.

Is there any specific reason to have only Enterprise level pricing?

They are optimizing for the scarce resource - which is time, since they got money in abundance.

You can query Grok on recent news and it will deliver the very recent updates (1 day old or probably even less)

This was my experience as well.

Gemini performing the best on coding tasks, while giving underwhelming responses on recent news.

While Grok was OK for coding tasks, but being linked to X, provided best response on recent events.

Social media is becoming TikTok’s clone army, with algorithms hooked on short-form videos for max engagement.

Text, images, and long-form content are getting crushed, forcing creators into bite-sized video to be favored by almighty algorithm.

It’s like letting a kid pick their meals - nothing but sugar and candy all day.

Ironically, the advancements in AI R&D and LLMs by Google and Meta were largely funded by their advertising-driven business models.

Owners residency/citizenship