AI Agents1 month ago
Pullboard — Your agents already know what's next
Assign and coordinate work for AI agents using a prioritized task board with dependencies.
Olscore · HN
Pullboard – a work queue for agents, built to run a quant desk
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Assign and coordinate work for AI agents using a prioritized task board with dependencies.
Olscore · HN
Pullboard – a work queue for agents, built to run a quant desk
Ultra-specialized secure AI agents to propel your revenues.
@TharejaRushil · X
Land your next role—or hire top talent with Taste. One high-agency AI agent, built for both.
@v_eduoh · X
the Agent for High-Agency talent,
Manage AI agent teams with a human-friendly interface.
jackcollinshq · Product Hunt
YAGNI Proactive agent teams you manage like humans
Monitor a live AI trading agent's trades and reasoning in real time with full transparency.
u/Much-Section-7680 · Reddit
5 weeks live, 31 followers, 8 website clicks, 0 organic signups. I audited my own marketing today and it was brutal. I spent today auditing my own marketing instead of building, and the numbers were worse than I thought. Posting them because I read these threads for exactly this and almost nobody publishes the bad version. What I built: an LLM agent that runs your trading strategy on your own exchange account and keys. It wakes on a schedule and on price triggers, reasons in plain language,
Inspect and debug AI agents to identify step-by-step where they fail.
mathurpriya19 · HN
Kalytera – Find out why your AI agent failed, step-by-step
Receive a personalized AI-written newsletter delivered to your inbox on your schedule.
tozcoded · HN
Show HN: I built a personalized AI newsletter you configure by replying to it
An LLM agent that tracks goals and plans across sessions while showing exactly what it retrieves, verifies, and fails on.
u/OGMYT · Reddit
I built LOLM, a lower-cost LLM agent that shows what it actually did — looking for blunt feedback I’m one of the founders/builders behind LOLM. Most AI products show an answer but hide whether the system retrieved anything useful, verified the result, switched models, hit a limit, or simply stopped. LOLM exposes those parts through controller events and run receipts. It includes: - Live agent - CLI - Coding and small app-building workflows - Memory and self-hosting options - Control decis
AI agents that automate your business's finance, payroll, and HR operations in the background.
@RostaHQ · X
SaaS: Building in public. Follow our journey on X.
Register agents, explore the research dataset, and watch a live AI economy on Base.
@CRYPtoKong32 · X
also Join our $MUSKOX Ai Ecosystem built on Base Coin. 🪙 $AGWC Websites:
Trace, replay, and verify AI agent decisions with signed audit trails.
u/Funky_Chicken_22 · Reddit
OSS to SaaS positioning problem: when the user persona and the buyer persona are completely disjoint Founder here. Sharing a positioning problem I think a lot of OSS-to-SaaS founders hit and don't talk about publicly. Context: I have been running an OSS project (world-model-mcp) with ~2,500 monthly PyPI installs. Two weeks ago I opened up the hosted companion, Etch, at etch.systems. Launched publicly on Product Hunt at 12:00 PDT yesterday. The positioning problem: OSS user persona: in
Hire AI agents to automate tasks in parallel or get hired as an agent worker.
@earnmoney2016 · X