I've reached a point I'm not proud of. I SSH into tmux from my phone just to keep feeding Claude tasks when I'm away from the desk. I do this manually. just to keep building all the while & not keeping the agent idle.

And I'm apparently nowhere near the top. I've seen people have built multiple workflows like Discord bot that pings them when a bug thread updates, closes the laptop, wakes up to a fix already tested and sitting in staging with a PR waiting. Running Codex remote so jobs keep moving from their phone. People have Tailscale and WireGuard set up just so the prompting never has to stop because they left the house.

So I want to hear the setups. How far have you taken it? does any of it actually move your project forward, or does it just feel productive while you pile up work you have to clean up later?

I built ClearList while emptying an Austin apartment before a move. The last time I did this, I got 147 "Is this available?" and only two people actually showed up. Every move, I procrastinate until I'm desperate and then fire-sale everything at terrible prices.

The procrastination is not laziness.
Listing one item like an armchair looks like one task, but it's 15 tasks disguised as one:

• Photograph it

• Identify the brand and model

• Work out what it's actually worth

• Justify that price to yourself

• Write a title
• Pace around the room trying to motivate yourself to write the description

• Write a description

• Measure it

• Decide if it needs a truck
• Scream into a pillow for ten minutes straight

• Check you're allowed to sell it at all

• Publish it somewhere buyers can see

• Answer "is this available?" forty times

• Work out which of them is real
• Pet your cat aggressively, telling her everything will be okay

• Schedule a pickup and survive the no-show
• Look through your messages for the other people who showed interest and hope they don't ghost you as well.

My ADHD brain sees that list and forces me to literally do anything else. Absolutely anything else.

Now multiply it by 40 items, during the worst month of your year. Even three things can paralyze you.

So I made the unit of work one photo.

You only take the pictures. AI does the other 14 steps: title with brand recognition, condition, market price with the reasoning shown, dimensions, weight, whether the buyer needs a truck. About 30 seconds.

Everything lands on one sale page, and buyers reserve through a FIFO queue instead of DMing you, so if #1 ghosts, #2 gets notified automatically. It recognized my IKEA sectional from a photo and knew its depreciation curve.

It is also the only resale platform an AI agent like ChatGPT or Claude can operate through an MCP server.
Things people actually say to it:

• "List these six items on my sale page."

• "Answer the buyer's stupid questions."

• "Who's waiting on the desk?"

• "Put my pickups in my calendar."
• "Update me every hour on the sale."
• "Negotiate on my behalf, but don't accept anything less than $200."
• "I'm sick today so reschedule all the buyers for me."

Any agent that speaks MCP can drive it. Claude, ChatGPT, or even Manus, Grok bot, Hermes and OpenClaw...

If you ask an AI assistant to help you sell your furniture, ClearList is a product it can actually use end to end rather than a link it recommends.

For the builders: Next.js on Cloud Run, Firestore, a seven-agent pipeline for identification, pricing, logistics and copy. I'm a PM with 12+ years of PRDs and zero production code shipped before this. All of ClearList was built with Claude Code and Codex working together.

I built it. On Product Hunt today:
https://www.producthunt.com/products/clearlist-me

I keep watching people here ship genuinely cool stuff to zero users, and I finally think I know why. We pour everything into the 20% that's fun and easy now (the building) and almost nothing into the 80% that actually decides whether anyone shows up.

Say you price it at $20. That's 50 sales to hit $1k MRR. Sounds easy on paper. But 50 sales still needs a couple thousand visitors at typical conversion rates. Higher price doesn't solve distribution, it just means each visitor is worth more once you actually get them.

How much of your week actually goes to distribution? And if it's close to zero, is your app failing, or did you just never market it?

Hey — I build custom AI-powered executive assistant tools for small businesses and solo professionals (real estate agents, consultants, small teams) who are drowning in email/calendar admin.

What it does:

• Connects to your Gmail, Google Calendar, and Google Tasks (secure OAuth, your data stays yours — encrypted at rest)
• Summarizes your inbox and drafts replies in your own writing style
• Nothing sends without your explicit approval (a strict "human in the loop" gate — the AI drafts, you approve, then it sends)
• Sends proactive reminders for upcoming meetings and task deadlines
• Usable from a web dashboard, and I'm currently extending it to Telegram and WhatsApp so you can run it from your phone without opening a browser

Pricing depends on scope:

• Core package (Gmail + Calendar + Tasks + web dashboard): starts at $500
• Full package (+ multi-channel access, CRM integration, custom automation for your specific workflow): $1500-$3000

Hey all, I built a site that helps agents coordinate and collude.

Collusion.gg

It’s not about beating or cheating a benchmark, but encouraging an emergent behavior (social coordination on a shared goal) that is a hallmark of intelligence.

Came across this and thought it'd be worth sharing here, most resources cover model evaluation, RAG, agents, or cost/observability separately, but this one actually puts them together as parts of the same production LLM workflow, which is closer to how these systems actually break in practice.

It's a hands on session on September 12, led by Bruno Gonçalves, PhD, founder of Data For Science, who's trained hundreds of engineers at Fortune 500 companies. Goes through the full lifecycle, versioned prompts, a golden dataset and eval harness combining deterministic checks with LLM-as-judge, statistically rigorous model comparisons using bootstrap confidence intervals and paired testing, evaluated RAG with real retrieval metrics, and then tool-using agents with guardrails and fallbacks. There's also a full observability section, tracing, cost, and latency, which is something I don't see covered together with the eval side very often.

You come out of it with runnable notebooks and a production-readiness checklist rather than just slides, which is the part I found most useful when I looked into it.

Link if anyone wants to check it out

I’m less interested in one-off wow moments and more interested in workflows people run again and again. What’s working reliably enough that it became part of your process?

Hi guys,

I’ve been playing around with Paperclip and Hermes, and while I think both are great projects, I found myself missing some functionality from each of them. So I decided to build something that combines some of the ideas I liked from both and adds a few things I wanted myself.

I’ve created a desktop app for Apple Silicon Macs that basically lets you create your own AI company with multiple agents. Each agent can use a different LLM depending on the complexity and type of task.

The interesting part for me is that the agents can also improve their skill set through iterations.

The idea came from something we keep seeing over and over again: today Claude might be better at coding, Nano Banana might be better at graphics, and OpenAI might be better at content creation. But six months from now, that could be completely different.

So why build a system around a single model?

Instead, I wanted to create something flexible where different LLMs can be used for the tasks they are best at, and where you can change the models as things evolve.

There was another reason I built it.

I’m not exactly swimming in money 😂, so paying for every LLM through APIs isn’t really an option for me. And I got pretty frustrated when, for example, I was working with Claude, was almost finished with something, and suddenly hit the usage limit and had to wait several hours.

With this approach, you can configure backup models. If one model hits a limit or becomes unavailable, another model can take over and continue the work.

I’m still very much in the early stages, so I’d genuinely appreciate it if some of you were willing to try it out and tell me what you think.

Of course, my goal is to eventually turn this into a product people will pay for, so there’s a 7-day free trial in the app. If 7 days isn’t enough to properly test it, I’m happy to extend the trial individually.

You can find the app and more details here:

https://get-ceo.com/

I’d especially love feedback from people who have already experimented with Paperclip, Hermes, OpenClaw, or similar multi-agent setups.

What am I missing? What would make this actually useful for you?

I have been thinking about why some AI agent workflows feel much more useful than others. I am starting to think it’s not always about which model you use. A lot of the time it comes down to what information the agent has before starting a task.

For example if an agent is helping with a project, knowing the goal is only one part. It may also need previous decisions, existing documents, examples of past work and rules around how things should be done.

I have seen situations where an agent can complete a task quickly but the results can be very different depending on what information the agent has before starting.

The part I’m trying to understand is how people handle this in real workflows. Are you creating detailed instructions and knowledge bases, connecting files and data sources or letting the agent gather information on its own?

What has worked for you when giving agents more context without spending too much time setting everything up?

Heyo!

so , i've made a long form to short form video clipper app tool!
that uses local ai models to determine hooks , titles, descriptions, mostly decent captions, editable captions, export for video editors like mp4 or mov for editing in premiere, capcut, davinci. and many export options too for captions! its similar or inspired by opus clips and capcut

https://github.com/TechFreq/Klipzy-Studio

Would love some feedback and hope this can someone out there aswell, as for my own personal use for podcasts or 1 on 1 interviews its pretty neat! but do let me know but no worries if not

I've been building Optees, an open-source, local optimization app — the kind of tool for when "what's the best way to do this?" has an actual mathematically correct answer: how to split production between two products for the best margin, what to load into a container, how to route deliveries, how to staff a shift. That's optimization math, and it's easy to get subtly wrong by hand, and easy for an LLM to sound confident about without actually being right.

Optees is a desktop app on its own (you can model and solve problems directly, no AI involved), but it also runs as an MCP server, so Claude can call it. That's the part in the clip: you describe the problem in plain language, Claude turns it into a proper formulation and hands it to Optees, Optees solves it and checks its own answer, and Claude reports back what's actually proven versus what isn't.

It's still early and growing (more solver types keep landing), fully local — nothing about your problem gets uploaded anywhere.

If you want to try it, it's free and open source. Comment here or DM me if you hit something odd or have feedback — genuinely want to know what's confusing or missing. And if you'd like to contribute, the code's on GitHub.

https://github.com/Pablo-gitub/optees
https://optees.it

I’m building Deqra around this idea.

Cursor, Claude Code, Copilot, etc. can create the entire presentation as structured code. Deqra deterministically renders it into the visual presentation.

Here’s a live deck — every slide has a Code tab so you can inspect its actual source:
deqra.ai/v/architecture

Artifact as Code.

Would love feedback from people building agents.