标签：#ha

"Stop summarizing the noise. Start executing it." Tsundoku Slayer is an autonomous agentic system powered by Hermes Agent that overnight patrols your unread tabs, mercilessly filters out 90% of the information overload, and saves only the information capable of killing your current blocker. 🎯 The Problem While debugging a painful Streamlit IndexError, I realized my real issue wasn't a lack of information—it was too much information. I had documentation, API feeds, tech news, and bookmarks all competing for my limited focus. Most AI tools try to "summarize" everything, which ironically generates more text to read and increases cognitive load. I didn't need another summarizer. I needed an autonomous agent capable of deciding what NOT to read right now. 🧠 How Hermes Agent Drives the Workflow This project doesn't just scrape webs; Hermes Agent acts as a high-conviction decision maker. It coordinates the entire workflow by running a multi-step reasoning loop overnight. ⚙️ The Agent Workflow Retrieve: Fetches unread article content via web scraping tools. Compare: Ingests and cross-examines the content against the user's active, real-time problem context (e.g., specific stack traces). Reason: Analytically evaluates the true relevance of the article to the current blocker. Verdict: Produces a high-conviction binary choice: SAVE or EXECUTE. Justify: Generates a crisp, logical explanation for why an article was terminated or spared. Synthesize: Automatically crafts an immediately applicable Python/Streamlit code patch for saved items. 📋 Example Outcome: Focus in Action Here is a real-world scenario of how Hermes Agent processes a chaotic backlog when you are stuck on a critical crash: Current Blocker: IndexError: list index out of range inside a Streamlit dialogue array loop. Unread Queue (Input): Streamlit st.status Documentation ➔ EXECUTE (Irrelevant UI reference) General Python Tag Feed ➔ EXECUTE (Too broad, pure noise) Tech News Flash ➔ EXECUTE (Complete distraction) Str

This is a submission for the GitHub Finish-Up-A-Thon Challenge The Bug That Doubled Real Trades On May 21, my live trading bot generated six duplicate execution attempts in one session. SafeAgent blocked all six. Without the guard: one duplicated a $1,350 sell another doubled a TQQQ position total duplicate transaction exposure: $3,653 That session changed how I think about AI agents, retries, and execution guarantees. What I Built SafeAgent is an exactly-once execution guard for AI agents and SaaS applications. It prevents duplicate payments, emails, trades, and webhook processing when retries fire after a timeout or crash. Live endpoint: https://safeagent-production.up.railway.app GitHub: https://github.com/azender1/SafeAgent PyPI: pip install safeagent-exec-guard The Comeback Story How it actually started Six months ago I was building two things at once: PeerPlay — a patented P2P wagering exchange for skill-based video game tournaments (USPTO provisional 63/914,036) — and a live QQQ/TQQQ momentum trading bot running on Alpaca Markets. Both hit the same bug. Contest verification agent times out, retries, settlement fires twice. Bot order fills, confirmation drops, retry fires, doubled position. Same failure mode. Different domain. Different models pushed me toward very different architectures during development. Some were fast but overconfident. The most useful moments came when a model explained why an approach was broken before I implemented it. That's part of why SafeAgent sat unfinished. Not just time — wrong turns that burned momentum. Why local idempotency fails Early versions used a local SQLite guard. It worked until it didn't: workers restart and the in-memory state is gone containers reschedule and replay from the last checkpoint retries land on a different machine entirely Exactly-once semantics require a durable coordination boundary outside the worker itself. That's what the hosted /claim endpoint provides — the claim lives on the server, not in the p

There's a question that keeps surfacing across engineering teams — sometimes in architecture reviews,...

don't want to file a decom report with a gap so I figured I would ask here. On a contract job clearing out a data center doing routine stuff like taking inventory and audits before we decommission hardware. The issue is there is one node that keeps coming back that isn't in the documentation. ip is in the 46.28.x.x range Its not in the facilities registry though. Ran it through RIPE and ARIN to find nothing. The latency is what is getting me though. 0.4 round trip every time. Tested from multipl

A crack in an aircraft window, a fracture starting at a bolt hole, a shaft that snaps at the shoulder where the diameter steps down. These failures share a cause that has nothing to do with the average load the part carries. The metal broke because a change in geometry concentrated stress into a tiny region, and that local peak — not the nominal stress — drove the crack. This article explains the stress concentration factor: what it means, where the classic value of 3.0 comes from, how to apply it, and the mistakes that make engineers underestimate the danger of an innocent-looking hole. Why this calculation matters Real parts are not smooth bars. They have holes for fasteners, fillets where sections change, keyways, grooves, threads, and shoulders. Every one of those features disturbs the flow of stress through the material. Where the lines of force have to bend around an obstacle, they crowd together, and the local stress climbs well above the value you would compute from force divided by area. The stress concentration factor, K_t, is the multiplier that captures this. It matters most for two failure modes. Under static loading of a brittle material, the peak stress can trigger fracture before the bulk of the section yields. Under cyclic loading, the concentrated stress is where fatigue cracks nucleate — and the vast majority of fatigue failures begin at a geometric discontinuity. If you size a part on nominal stress alone and ignore K_t, you have skipped the step where most failures are actually decided. The core formula The stress concentration factor is defined as a simple ratio: K_t = sigma_max / sigma_nom Here sigma_max is the true peak stress at the discontinuity and sigma_nom is the nominal stress computed from elementary mechanics. The subscript t means "theoretical" — K_t depends only on geometry and loading mode, not on the material. It comes from elasticity theory, finite element analysis, or experiment, and it assumes the material is still behaving ela