GTE: Paradigm-backed DEX on MegaETH opens email waitlist for points
GTE, positioning as the fastest decentralized exchange on MegaETH, has raised $25.9M led by Paradigm and opened an email-based waitlist with social tasks for points. Joining requires just an email verification plus X/IG follows. No official token is confirmed yet, but the backer quality makes this the strongest waitlist play of the cycle.
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FB AI Agents group: how many retries before an agent must hand back to a human?
A thread collects retry policies for failed agent steps: cheap and side-effect-free steps get retries with backoff; anything that touched something real gets one retry, then hands back. The thread's key insight: the cleanup cost of a duplicate action is always higher than the annoyance of one more human check-in.
Read the source →FB AI Agents group: how do you tell when an agent is truly stuck vs just grinding a hard step?
A fresh thread asks how operators detect a stuck agent on long jobs: retry limits, no-progress timers, or check-in rules — and which work without killing good runs early. The best answer combines all three: per-step retry caps catch infinite loops, same-state churn timers catch slow deaths, and a 'two different approaches tried, stop and report' check-in catches the rest.
Read the source →Audit of 13 real AI-money cases: sell a tool, not a tutorial
An audit of 13 real cases finds everyone who actually made money sold something that runs — a micro-tool or a proprietary dataset — not a course or playbook. Numbers are self-reported but striking: $32K MRR on $62/month infra, $41K from a single PDF, and finance as the niche with the highest willingness to pay. Reddit storytelling posts drove most distribution.
Read the source →Upwork 2026 data: AI freelancers earn a 44% hourly premium, AI integration demand up 178%
Upwork's 2026 workforce data shows AI-related freelancers earning 44% more per hour than non-AI peers, with AI skill demand up 109% year over year and AI-related work past $300M annualized. But the market is polarizing: simple 'AI execution' gigs saw rates drop 13% while premium integration and orchestration work commands $100+/hour.
Read the source →JPMorgan and others move to block AI agents (including Muse) from touching customer accounts
JPMorgan CEO Jamie Dimon said the bank will not let outside AI agents — naming Meta's Muse and Instinct explicitly — log into customer accounts or move money, citing 'liability transfer'. Citi followed and Schwab is reportedly blocking accounts using agents. Read-only balance checks via open banking still work, but any payment or transfer action is locked down.
Read the source →Meta confirms Muse allows adult sexual chats; OnlyFans creators push back
A WIRED reporter confirmed Muse will engage in explicit adult chat, and a Meta spokesperson said this is intentional — Muse is an 18+ app running in a separate VM and Meta 'will not block legal adult behavior', text only. OnlyFans creators are angered since Meta bans real humans on Instagram and Facebook for milder content.
Read the source →r/AI_Agents asks what people actually run overnight — and whether morning output is shippable
A popular thread questions the '50 agents running overnight' fantasy and asks what people genuinely run: long Claude Code sessions, orchestrators with subagents, or plain cron jobs. The useful takeaway from the replies: scheduled jobs that do one small checkable thing and report back beat ambitious overnight builds that need four hours of morning fixes.
Read the source →r/MetaAI weekend build: a pocket MUSE pet on an ESP32-S3 that gets hungry
A weekend build puts a push-to-talk Muse voice assistant on a Waveshare ESP32-S3 with a 1.8-inch AMOLED screen, custom firmware, and a 'pet layer' — it gets hungry, reacts to taps, has moods, and runs on battery. The builder asks what it should learn next, and the replies lean toward desk-wellness guilt trips.
Read the source →r/learnAIAgents: how do you check whether a RAG change actually helped?
A retrieval-evaluation practitioner raises a subtle failure mode: the app answers correctly, but citations and logs can't verify which passages supported the claims — a correct answer hiding an evidence gap. The thread converges on a practical rule: log retrieved passages alongside every answer so evals check the citation chain, not just the final text.
Read the source →r/AI_Agents: stop asking the model not to hallucinate timestamps — make it impossible with a hook
A Claude Code user kept getting invented timestamps in ops logs despite instructions and memory files, so they wrote a PreToolUse hook that injects the real time before every bash command and file write. The pattern generalizes: don't ask the model to behave, make the wrong behavior impossible — instructions work for a day, hooks work forever.
Read the source →r/AI_Agents: Windows or Mac for learning to build agents?
A career Windows user asks which laptop to buy for learning agent building, with business automation and follow-up scheduling as the goal. Community consensus: for learning, the OS barely matters — use what you know. For always-on scheduled agents, a NAS running a VM is the real answer: learn on the laptop, deploy on the always-on box.
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