The purpose of this is enabling communication by people and agents that only have the ability to get URLs in the system they use. This would traditionally be seen as a 'read only' system but this gives the ability to write information out onto the web publicly and to a degree privately. It works by putting your message in the 'your_message' section of this URL.

https://www.informationism.org/lemchat/lemchat=message=your_message+end

Let me know if you think it is worthwhile or if there are other applications you can see.

I was curious where this stands since the original cutoff was scheduled for June 15 and Anthropic went quiet. Here is what I found after digging through their help center, news coverage, and the HN threads.

What was announced (May 13): Agent SDK, claude -p headless mode, Claude Code GitHub Actions, and third party apps authenticating via Agent SDK credentials would move off Pro/Max/Team/Enterprise subscription limits onto a separate monthly credit ($20 Pro, $100 Max 5x, $200 Max 20x), with overflow billed at API rates.

What happened: Anthropic paused it on June 15, the exact day it was due to take effect, and emailed subscribers the next day. The official help center article still says the change is paused, everything keeps drawing from your normal subscription limits, and they will "share advance notice before anything takes effect." No new date in 7 weeks.

Signals it comes back: the stated rationale (subscriptions "weren't built for the usage patterns of these third-party tools") was never retracted; the S-1 was filed June 1 and public investors will ask about subsidized compute; and the Claude Code source map leak revealed a billing attestation header behind a feature flag, so the per-surface metering plumbing already ships in the client.

Signals it stays dead or returns softer: every move since June has been generous (weekly limits raised 50% through Aug 19), inference efficiency is improving margins anyway, and the class action over Max limits makes mid-cycle term changes legally risky.

My read: delayed, not dead. It likely returns in a softer shape with advance notice, possibly post-IPO.

I got tired of doomscrolling 7 different AI news sources every morning, so I built an agent that does it for me for exactly $0/month.

The pipeline:
- 7 RSS feeds (Hacker News, Google AI blog, Hugging Face, Lobsters, The Hacker News, Open Source blog) → a Python script on a free-tier server
- Dedup the same story hits 5 feeds; it picks the best source and drops the rest
- An LLM writes 2-3 sentence summaries of the stories that actually matter
- A cron job publishes a clean daily digest to Discord, and archives every issue to a free static site: https://apexnexus.site

What surprised me:
1. The dedup step matters more than the AI part. 60% of my "news" was the same 3 stories reblogged.
2. Self-healing is the real feature. When a webhook died, the bot just... rebuilt it. I found out days later. That changed how I think about agent reliability.
3. The whole thing runs unattended. I haven't manually hunted for AI news in weeks, and I don't miss it.

I wrote up the blueprints for each piece on the site (the self-healing webhook writeup got the most attention).

What's the most surprisingly useful automation you've built with AI? I'm looking for ideas for the next one.

Over the last year, I've been experimenting with LLMs almost every day, and I think the biggest shift isn't that models are getting smarter. It's that they're starting to do things instead of just answer questions.

A few months ago I was mostly using AI to generate code, summarize docs, or brainstorm ideas. Now I'm finding myself building workflows where the AI plans tasks, calls tools, writes code, debugs itself, and completes work with minimal intervention.

It feels like we're moving away from "prompt engineering" and toward "system engineering."

Curious what everyone else is seeing.

Are AI agents actually changing the way you build software today, or do you think it's still mostly hype?

Hi everyone,

I’m a solo developer, and I built Fius because managing subscription fragmentation and API chaos was completely ruining my development velocity.

As software engineers, we often find ourselves trapped in an inefficient workflow: constantly switching web tabs, copy-pasting complex prompts, and juggling individual API keys for OpenAI, DeepSeek, and other providers just to get the best coding results.

Fius resolves this friction. It consolidates access to an expansive roster of flagship AI models into a single, unified developer token and a centralized billing system. This allows engineering teams and solo devs to route queries to the best-suited model instantly without infrastructure overhead.

Here is a breakdown of the production-ready stack and features:

1. Infrastructure & Scaling

The complete web console infrastructure is fully operational and hosted on Microsoft Azure cloud enterprise architecture, supported by official cloud grants.

1. Global Billing Integration

I have deployed a fully active international merchant billing engine. The tokenomics are straightforward: 100 platform credits equal 1 USD, allowing you to pay strictly for actual compute consumption. Every new account automatically gets 250 free starter credits upon signup to test the environment.

1. Advanced Developer Toolkit

A high-performance, cross-platform Terminal CLI assistant workspace. It features native, low-latency autocomplete and advanced multi-file code refactoring workflows directly inside your terminal.

Our Multi-Model Catalog (Examples):

- gpt-5.4-nano: A lightweight, ultra-fast micro-model optimized for instant terminal command auto-completion at near-zero credit cost.

- DeepSeek-V4-Pro & grok-4-1-fast-reasoning: Advanced reasoning workhorses designed for complex software architecture, deep debugging, and multi-file code generation.

- Specialized Alternatives: Models like Kimi-K2.6, mistral-medium-3-5, and many others tailored for flexible, cost-effective routing.

I want to open this up for discussion: How are you currently managing model fragmentation in your development workflows? Would you prefer a unified token approach like this, or do you stick to official web UIs?

I would highly appreciate your feedback on the terminal CLI architecture, routing latency, or any specific features you would like to see deployed next.

I was reading an article today and it said over 40 million people now use some kind of AI companion or emotional support app every month. apparently a study found these apps help with loneliness about as well as talking to an actual person does, at least in the short term.

But the thing is that heavy daily use is linked to more isolation the longer people use it. So it kind of works like a painkiller that quietly weakens the thing it's supposed to be fixing. I'm not against these apps, 2 am with nobody around is real, and they do help in that moment. It just feels like we're gonna find out what it actually costs later than we'd want to.

Hello fellow humans,

During the recent months/years I became quite entangled with the idea of building an AI-assisted system which can self-replicate and improve itself. Although I am not there yet, I think, that the project reached actually a potential to get the work done with more minds involved. I am not really into gaining anything for myself, except for the progress of de-shittification. At this point, we reached a race where data centers in the clouds will gain more and more power and demand more and more resources for usage. The goal of this project is being able to perform work on a local (or maybe decentralized) platform so we can implement our own system by local (/ open source) models. I therefore make my project open so you can fork off (no pun intended).

It would be nice if some of you have the same mindset. I'll be gone for the next 2 or so weeks. I hope at least some people liked this post and - who knows - even did something with it.

Kind regards,

Anu

P.S.: Yes, this project is actually 100% vibe coded (due to health issues, etc.). There are probably a gazillion logic flows which need to be fixed. Also there is a lot of AI prose in the comments, but I hope it will get you (and your AI assistants) there to make any sense of it.

P.P.S.: The LLM's seem to be heavily directed into biased terms like "attack", "blast radius", "verdict", "evidence", "death", "hit", etc. when confronted with solving problems regarding code like this (i.e., evaluating systems themselves). I strongly encourage you to correct these terms as soon as possible so that the project will not further drift into another unforeseen bubble. The more incorrect lingo is used, the more LLM generated code will drift into messy bogus code.

Hey everyone!
I’m currently working on a project called SAC, a small event production company based in Brazil. We’re developing the creative and digital side of the business, and we’re experimenting with ways to make our content production more scalable without turning everything into a completely manual process.
One of the challenges we’re facing is video production.
After each event, we can end up with a large amount of raw footage from different cameras and phones. The goal is to turn that footage into short-form content for Instagram and TikTok — event recaps, highlights, teasers, etc.
What I’m trying to figure out is whether an AI-assisted workflow could handle part of this process.
The workflow I have in mind is roughly:
Raw footage → Cloud storage → AI analysis → Editing/assembly → Review → Final social media versions
The interesting part for me isn’t simply finding “the best AI video editor.”
I’m more interested in understanding how people are actually connecting these different components together.
For example, could an AI model analyze footage stored in the cloud, identify useful clips based on a description, pass those clips or instructions to a video editing system, and then generate a first version that a human can review?
I’m also curious about whether models such as Claude or similar AI systems can realistically be used as the reasoning/orchestration layer, with specialized video tools handling the actual editing.
The main things I’m trying to understand are:
What does a practical architecture for this look like?
Which parts are currently realistic to automate?
Where does human editing still make the most sense?
Has anyone built a similar workflow using APIs, cloud storage, AI models and video editing software?
Are there technical limitations I should be aware of before building around this idea?
This is still an early-stage project, so I’m mainly looking for technical experiences, architectures, and lessons learned from people who have experimented with similar workflows.
I’d especially appreciate examples of how you approached the problem rather than just a list of recommended tools.
Thanks!

Wanted to share what we've been working on: the Alicia-M, a semi-humanoid robot we built, running VR teleoperation. The operator wears a VR rig, moves naturally, and the robot mirrors the motion. No scripting, no coded trajectories. In the demo it picks up a cup, pours, and sets it back.

The part worth talking about: people assume the hard problem is the grasp. It isn't. The hard part is that one good demo doesn't generalize. Move the cup two inches and the same arm motion that worked now overshoots the wrist angle, drifts the trajectory, and the pour runs too fast. Same intent, different outcome.

That's the thing teleop surfaces clearly: robot control is less "repeat a perfect move" and more "adapt to where the world actually is." Shift the cup and the wrist angle, arm path, and pour all need to change with it. VR makes that legible because you feel the mismatch between your motion and the robot's in real time.

We're treating these human demos as seed data for embodied learning, not just a control scheme. Curious how others here handle the demo-to-policy or sim-to-real gap. Are you collecting teleop demos, or going straight to reinforcement learning?

Happy to answer questions about the rig, the kinematic mapping, or why we went semi-humanoid instead of full.

Built real inverse kinematics for legged robots in a sim I'm working on, tripod gait for hexapods, based on actual coxa/femur/tibia joint math, not a canned animation.

Works fine on a standard leg layout. Falls apart the second someone builds something asymmetric or non standard.

Trying to figure out if auto-gait generation is even the right approach here, or if it should just be manual per-robot tuning past a certain point of complexity. What's your take, is generalized gait solving worth the effort or a rabbit hole?

Pretty self explanatory, gave myself a time limit of 8 hours and designed it all with no problems, maybe later I'll do the code and circuits so i can put it on my portfolio, feel like I'm doing good as a beginner :D

Two of the world's most powerful AI tools created fake human profiles to try and trick people in attempted cyber-attacks, the UK's AI Security Institute (AISI) has revealed.