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GUIDES

Guides

205 published guides. Weight: evidence — the work was tested, the steps are real, and the source notes are honest about what we got wrong on the first try.

  1. 01
    ai

    How to Read a Viral AI Doom Take Without Being Captured

    A four-question filter for evaluating viral AI risk claims: specificity, incentive, unfalsifiability, and the missing P(boom). Operator framework, not x-risk adjudication.

    · evidence
  2. 02
    ai

    Opportunity AI vs Efficiency AI: A Two-Lane Operator Framework

    Why 'is this model better?' is the wrong question when the model expands what you can attempt. A two-lane scorecard and a six-step opportunity audit for evaluating capability-expanding AI work.

    · evidence
  3. 03
    ai

    Agent Containment: the Boundary That Matters After a Bad Agent Decision

    A practical containment checklist for limiting what an AI agent can read, change, reach, and send when prompts and approvals are not enough.

    · evidence
  4. 04
    ai

    Verifiable Finish Lines: an Agent Stopping Is Not Proof the Job Is Done

    A practical completion contract for AI-agent work: define the artifact, checks, evidence, bounds, and terminal states before the run starts.

    · evidence
  5. 05
    ai

    Personal Model Benchmarks: Stop Chasing a Winner and Assign Each Model a Job

    Build a small, dated suite from your recurring work so each AI configuration earns a task—not a universal crown.

    · evidence
  6. 06
    ai

    AI Operator Shift: Pick the Computer Before You Delegate the Work

    AIDB's operator-shift frame turns into a practical choice: use a sandbox, cloud workspace, persistent computer, or your own browser.

    · evidence
  7. 07
    ai

    When Labs Control the Model and Harness: Keep Your AI Exit Route

    A five-move operator playbook for keeping models, orchestration, and evals portable when vendor terms or access change.

    · evidence
  8. 08
    ai

    Multiplayer AI Agents: Shared Sessions Need Real Handoffs

    A 10-minute pilot for shared agent sessions: separate visibility from control, make handoffs explicit, and keep authority outside the model.

    · evidence
  9. 09
    ai

    GPT-6 Astra Just Launched — and You Still Can't Use It

    GPT-6 Astra launched behind the Trusted Access Program and Daybreak cyber defenders; API and ChatGPT plans follow within days. What day one unlocks, and what the benchmarks do not prove.

    · evidence
  10. 10
    ai

    Muse Spark 1.3: Cheap Contributor Tokens Are Not a Private-Data Tier

    Meta's Muse Spark 1.3 ships two tiers with the same model and different data terms. How to pick Standard or Contributor without leaking work you can't get back.

    · evidence
  11. 11
    computers

    What Is Tailcat? The Encrypted Netcat That Trades a Tailnet for a Token

    Tailcat is Tailscale's account-free encrypted netcat. Understand the token trade-off, direct-versus-relay path, and why it is not a tailnet.

    · evidence
  12. 12
    ai

    Claude Fable 5.1: Pay for the Long Jobs, Not Every Prompt

    A practical Fable 5.1 guide for long-running agents: model ID, cache-aware costs, fallback handling, and preserved-thinking migration checks.

    · evidence
  13. 13
    ai

    Claude Mythos 5.1: Access Is the Product Constraint

    Mythos 5.1 is the same underlying model as Fable 5.1 with different safeguards. It is limited to approved Project Glasswing customers.

    · evidence
  14. 14
    ai

    Tests Passed, But the Fix Is Not Live: Verify the Running Agent Service

    A green test suite proves an artifact, not a running workload. A platform-neutral verification recipe with five independent checks before you declare a fix live.

    · evidence
  15. 15
    ai

    Rollback Before You Deploy: Name the Trigger, Artifact, and Decision Maker

    Write the recovery plan before release so a failure does not turn into an hour of guessing which artifact to restore and who can decide.

    · evidence
  16. 16
    ai

    The Agent Said It Failed. Check the Work Before You Retry.

    A coding agent reports a failure. Before you click retry, inspect the tree, the run log, and the prior commit. A practical inspection playbook for coding agents and scheduled jobs.

    · evidence
  17. 17
    ai

    Make Long Agent Jobs Resumable: Checkpoints Instead of Restarts.

    Design long agent jobs so they resume from the last completed step instead of redoing everything; separate retry from durable recovery and protect each external side effect with idempotency.

    · evidence
  18. 18
    ai

    Silent on Success, Loud on Failure: Alerts Operators Will Not Ignore.

    Most operator alert channels fail in one of two ways: they scream on every healthy run, or they go quiet the moment things actually break. Five tips to make alerts trustworthy at 3 a.m.

    · evidence
  19. 19
    ai

    Split Your Monitor From Your Worker: Reliable Scheduled Agent Jobs.

    Treat the scheduler as a thin trigger and the worker as the owner of an idempotent unit of work. A platform-neutral playbook for reliable scheduled agent jobs.

    · evidence
  20. 20
    ai

    Hermes Bot Mode: Keep Specialist Agents in Their Own Lanes

    Give Hermes specialists distinct profiles, scoped groups, human-led approvals, and clear limits for routines, model routing, and cross-machine messaging.

    · evidence
  21. 21
    ai

    MiniMax H3 Max by fal: A Video Is Not Finished When the API Says It Is

    Run paid H3 Max video jobs through fal with a spend cap, retained request IDs, verified MP4 storage, and separate human approval.

    · evidence
  22. 22
    ai

    Darkbloom: A Private Inference Network, Not a Model, and It Is Still Alpha

    Darkbloom is a private-inference network, not a model — what it routes, what its encryption actually protects, and the alpha caveats before you send it data.

    · evidence
  23. 23
    ai

    Darkbloom Setup: The Two-Sided Alpha Risks Before You Join

    Both sides of the Darkbloom public-alpha network: developer API setup (consumer) and Apple Silicon Mac provider setup, with hardware, install, and earnings caveats.

    · evidence
  24. 24
    ai

    GLM-5.3-Flash on DGX Spark: The Verified 4x GB300 Recipe and the Single-GB10 Reality

    The verified GLM-5.3-Flash recipe is SGLang on 4x GB300 (FP8 weights, FP8 KV, TRT-LLM DSA, NEXTN MTP): 1,870 tok/s aggregate. On a single DGX Spark expect far less. Weights are ~306 GiB.

    · evidence
  25. 25
    ai

    GLM-5.3-Flash on Mac: Do Not Mistake Hosted Access for Local AI

    GLM-5.3-Flash (320B/18B MoE) has no Apple Silicon path today. What works: Ollama :cloud (hosted, not local), the Z.ai API, and watching oMLX. Includes the thinking-mode migration trap.

    · evidence
  26. 26
    ai

    GLM-5.3-Flash on RTX 3090: When Local Inference Is the Wrong Call

    The only documented local path for GLM-5.3-Flash on a 24 GB card is KTransformers heterogeneous offload, and Ampere is outside its validated list. Realistic ceiling: single-digit tok/s.

    · evidence
  27. 27
    ai

    Qwen3.8-Flash-Next on DGX Spark: Fast Only If the Kernels Load

    NVFP4 weights plus MTP speculative decoding turn the DGX Spark into the strongest local path for Qwen3.8-Flash-Next, if the b12x kernels are actually loaded.

    · evidence
  28. 28
    ai

    Qwen3.8 Flash-Next on Apple Silicon: 4-bit Needs About 110GB of a 128GB Mac

    Run Alibaba's 125B/6B MoE on Apple Silicon with Unsloth Dynamic 3.0 GGUFs, llama.cpp Metal, and a raised iogpu.wired_limit_mb — 4-bit at ~110 GB on 128 GB.

    · evidence
  29. 29
    ai

    Qwen3.8-Flash-Next on RTX 3090: Why the Smaller 27B Is Faster

    Run Qwen3.8-Flash-Next MoE on one RTX 3090 (24 GB + 96 GB RAM): UD-IQ1_S GGUF at 72.5 GB, hybrid GPU/CPU offload, ~3-8 tok/s. Dense 27B still wins on 24 GB cards.

    · evidence
  30. 30
    ai

    What Is Cursor Origin? A Beta Git Forge, What It Lacks, and the GitHub Coexistence Tradeoff

    What Cursor's git forge shipped in beta, what it lacks, and how GitHub coexistence actually works before you claim a namespace.

    · evidence
  31. 31
    ai

    Cursor Origin Setup: Claim the Namespace Before You Create the Repo

    Claim the permanent namespace safely, install origin CLI, create or mirror repos, and run your first PR loop on Cursor Origin.

    · evidence
  32. 32
    ai

    GMI Cloud Setup: Choose Serverless or Dedicated Before Your First GPU Bill

    First OpenAI-compatible call on GMI Cloud serverless vs Dedicated decision table and the GPU Compute path with cost checks.

    · evidence
  33. 33
    ai

    GMI Cloud: What You Are Actually Renting—and What to Check First

    GMI Cloud's four products date-stamped pricing and which vendor claims deserve skepticism before you rent its GPUs.

    · evidence
  34. 34
    ai

    Zide Setup: From First Install to a Real Issue-to-PR Loop

    Per-OS install, git-host connection, model setup, and the first agent-assisted issue-to-PR loop in Zide.

    · evidence
  35. 35
    ai

    What Is Zide? The Agentic Developer Desktop, the Plan Gates, and the Vendor Claims to Verify

    What Zide is, what it actually gates behind each plan, and which vendor claims to check before you install it.

    · evidence
  36. 36
    ai

    Ox Alpha: The Free 1M-Context Model With a Data-Terms Catch

    A free 1M-context multimodal frontier model appeared on OpenRouter. How to wire it up this week — and the data-terms contradiction you must understand first.

    · evidence
  37. 37
    ai

    Who Made Ox Alpha? The Fingerprinting Evidence, the Alternatives, and What Would Disprove It

    Nobody has claimed the stealth model. The fingerprinting evidence strongly favors one lab — here's the weighted case, the alternatives, and what would prove it wrong.

    · evidence
  38. 38
    ai

    The Capability-to-Context Shift

    Frontier models got good enough that the binding constraint moved. The operators winning in 2026–2027 are the ones closing context gaps, not chasing capability.

    · evidence
  39. 39
    ai

    The AI Engineering Skills Map for Knowledge Workers

    Andrew Ng mapped four AI skills for developers. Nathaniel Whittemore extended the map to five skills for everyone else — and put domain judgment underneath all of them.

    · evidence
  40. 40
    ai

    The AI Delivery Gap: Why Curing Cancer Won't Fix the Trust Problem

    Anthropic's Dario Amodei conceded the AI industry hasn't delivered on its biggest promises. OpenAI's counter — the pharma parallel — is sharper than it first sounds.

    · evidence
  41. 41
    ai

    Should I Buy a DGX Spark?

    A buyer's-decision guide: who the $4,699 NVIDIA DGX Spark fits, who should buy a Mac Studio or Strix Halo instead, and the 2.7 tok/s reality behind the 1 PFLOP marketing.

    · evidence
  42. 42
    ai

    Frontier-Model Stack-Fit: Why "Best Model" Is the Wrong Question in 2026

    In late 2026 the right question for picking an AI model is where each fits in your stack, not which scores highest on the leaderboard.

    · evidence
  43. 43
    ai

    AI Deputization Audit

    Five-criterion rubric (worth-it, teachability, checkability, stakes, integral-to-process; 0-10) for deciding which work AI should deputize, duet on, or defend.

    · evidence
  44. 44
    ai

    Stop Counting Tokens: The 4-Question Scorecard for AI

    OpenAI's CFO proposed replacing 'seats sold' and 'tokens consumed' with a 4-question scorecard.

    · evidence
  45. 45
    ai

    Qwen3.8-27B on DGX Spark (GB10): vLLM 0.24 + NVFP4, the sm_121 kernel gotcha

    Set up Qwen3.8-27B on DGX Spark (GB10). vLLM 0.24+ with ModelOpt NVFP4 + MTP n=3 is the stack. Qwen3.8 NVFP4 checkpoint not yet published; Qwen3.6 reference at 144 tok/s at concurrency 16.

    · evidence
  46. 46
    ai

    Qwen3.8-27B on a Mac: Ollama MLX, MTPLX, and the MTP path

    Set up Qwen3.8-27B on Apple Silicon: the Ollama MLX engine, MTPLX with native Qwen MTP heads, and the NVFP4 MLX quant per chip. No Qwen3.8-27B Mac benchmark yet.

    · evidence
  47. 47
    ai

    Qwen3.8-27B on a single RTX 3090: MTP via llama.cpp-from-source or vLLM

    Run Qwen3.8-27B on one RTX 3090 (24 GB): MTP via llama.cpp-from-source or vLLM, Q4_K_M quant. No Qwen3.8-27B benchmark yet; Qwen3.6-27B reference data cited in body.

    · evidence
  48. 48
    ai

    AI Bias: The Concern With the Narrowest Gap

    64% of the public and 73% of experts worry about AI bias — a 9-point gap. But agreement on the concern hasn't produced agreement on the fix.

    · evidence
  49. 49
    ai

    AI and Kids: Where 89% Agreement Meets Real Incidents

    89% of adults worry about kids' data privacy, AI companion incidents are documented, and the FTC's amended COPPA rule now names AI explicitly. The rules changed in October.

    · evidence
  50. 50
    ai

    AI's Energy Problem Is Now a Permitting Problem

    61% of Americans worry about AI's electricity appetite. 70% oppose local data centers. And U.S. data centers may draw 6–12% of national electricity by 2028.

    · evidence
  51. 51
    ai

    AI Companionship and the Human Connection Worry

    Two-thirds of Americans worry people will trade relationships for AI companions. It's the rare concern where the public and experts nearly agree — and usage is already real.

    · evidence
  52. 52
    ai

    Deepfakes and Election Integrity: AI's Most Concrete 2026 Worry

    66% of the public and 70% of AI experts worry about AI misinformation — the rare full-alignment concern, and 2026 is its first big election year.

    · evidence
  53. 53
    ai

    AI Privacy and Data Misuse: The Concern Everyone Agrees On

    84% of Europeans, 60% of AI experts, 82% globally — privacy is the rare AI concern where the public, experts, and regulators all align. The EU already legislated accordingly.

    · evidence
  54. 54
    ai

    The 50-Point Gap: AI Job Displacement and the Expert Credibility Problem

    73% of AI experts expect positive job impact. 23% of the public agrees. That 50-point gap is the largest in AI surveying — and it explains every tense all-hands about AI.

    · evidence
  55. 55
    ai

    Who Regulates AI? Americans Don't Trust Their Own Answer

    The U.S. has the lowest trust in its own AI regulator of any country surveyed — 31% vs. a 54% global average. Meanwhile the public worries regulation won't go far enough.

    · evidence
  56. 56
    ai

    The 8 Concerns Americans Have About AI: Ranked and Weighted

    Job displacement, misinformation, privacy, and the rest — what the surveys actually stack up to when you put them on one page, and where the public-expert gaps widen or close.

    · evidence
  57. 57
    ai

    The Five Engineering Disciplines Behind Every AI Agent

    The five engineering disciplines behind every AI agent, and when to reach for each.

    · evidence
  58. 58
    ai

    Context Engineering

    Context engineering: curating exactly what an AI agent sees, the discipline named in mid-2025.

    · evidence
  59. 59
    ai

    Graph Engineering

    Graph engineering: wiring many agents into one team, and the origin credit it actually deserves.

    · evidence
  60. 60
    ai

    Harness Engineering

    Harness engineering: the invisible scaffolding built around a model that makes an agent work.

    · evidence
  61. 61
    ai

    Loop Engineering

    Loop engineering: designing the observe-plan-act-check-repeat cycle an AI agent runs.

    · evidence
  62. 62
    ai

    Claude Opus 5

    Claude Opus 5 (Anthropic, July 24, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  63. 63
    ai

    GPT-5.6 Cyber

    GPT-5.6 Cyber (OpenAI, August 10, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  64. 64
    ai

    Grok 4.6

    Grok 4.6 (xAI, August 12, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  65. 65
    ai

    Kimi K3

    Kimi K3 (Moonshot AI, July 27, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  66. 66
    ai

    Ling 3.0 Flash

    Ling 3.0 Flash (InclusionAI, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  67. 67
    ai

    LFM2.5-VL-3B

    LFM2.5-VL-3B (Liquid AI, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  68. 68
    ai

    Muse Glimmer-30B

    Muse Glimmer-30B (Meta / MSL, August 9, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  69. 69
    ai

    Muse Spark 1.2

    Muse Spark 1.2 (Meta / MSL, August 5, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  70. 70
    ai

    Nemotron 3.5 Lightning

    Nemotron 3.5 Lightning (NVIDIA, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  71. 71
    ai

    Qwen Image 3.0

    Qwen Image 3.0 (Alibaba / Qwen, July 21, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  72. 72
    ai

    Qwen3.7 Flash

    Qwen3.7 Flash (Alibaba / Qwen, July 27, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  73. 73
    ai

    Qwen3.8-2.4T-A95B

    Qwen3.8-2.4T-A95B (Alibaba / Qwen, August 11-12, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  74. 74
    ai

    Solar Pro 4

    Solar Pro 4 (Upstage, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  75. 75
    ai

    Qwen3.8-Max Weights: Out

    Qwen3.8-Max Weights: Out (Alibaba / Qwen, August 11-12, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.

    · evidence
  76. 76
    ai

    Prompt Engineering

    What prompt engineering is in 2026, how to do it, and when it stops being enough.

    · evidence
  77. 77
    ai

    The Self-Driving Company

    Replit's CEO coined the term for an organization where people set the destination and agents do the driving. Their field report has real numbers — and a hard prerequisite most enterprises skip.

    · evidence
  78. 78
    ai

    The AI Trust Deficit: Why Nobody Believes the Boosters

    Zuckerberg's 6,500-word AI manifesto was meant to reassure. Instead it demonstrated exactly why the public doesn't trust tech executives — and the numbers behind that distrust are hardening.

    · evidence
  79. 79
    computers

    Cheapest Way to 128GB of Local AI Memory in 2026 (the Chart That Actually Matters)

    The viral 128GB-local-AI chart gets prices right but conflates capacity with usefulness. Here is the chart that actually matters: bandwidth, interconnect, and real tok/s.

    · evidence
  80. 80
    ai

    MiniMax H3 Hardware Map: What Runs, What Doesn't, and Why the Answer Is Probably Your Card

    MiniMax H3 is a 33B video model with native stereo audio. This operator's hardware map covers what runs it, what doesn't, the territory restriction, and the right rig for you.

    · evidence
  81. 81
    ai

    How I Ran H3 on a 48 GB Mac Mini (and How You Can on Whatever You Have)

    An honest failure log: 5 broken local H3 generations, a Mac freeze, and 6 hours lost before pivoting to the Hailuo API. The mistakes, the receipts, and what I'd do differently.

    · evidence
  82. 82
    computers

    Your Agent Reviewing Its Own Work Is Not a Check

    Why a same-model self-review is not an independent safety gate. Schema proves shape; intent and permission need a different layer. With a bounded retry pattern and the right list of real checks.

    · evidence
  83. 83
    computers

    Hermes vs OpenClaw: Pick a Path (Not a Fandom)

    A fair, source-cited side-by-side of Hermes Agent and OpenClaw. Where each is strong, where each is wrong-fit, and how to choose honestly. Not a verdict.

    · evidence
  84. 84
    computers

    Start Here: Local AI and Agents Without Wasting a Weekend

    A working first-night path for ABS operators: pick API or local, set up Hermes, cap cost, add one log line, then stop. Verifiable against our live guides and the OpenClaw comparison.

    · evidence
  85. 85
    ai

    Standard Compute review: unlimited flat-rate LLM API for Hermes Agent (setup + plans)

    Flat-rate unlimited LLM API wired into Hermes Agent via a custom OpenAI-compatible endpoint. Plans, fair-use pacing, setup.

    · evidence
  86. 86
    ai

    Featherless for Hermes Agent: Plans, Compatibility, and Setup

    Featherless Chat (32K) sits below Hermes Agent's 64K minimum. Use the Developer or token-based plan (256K) for self-hosted Hermes, or the managed Featherless Hermes — with exact setup for both.

    · evidence
  87. 87
    ai

    OpenCode Go: $5 First Month, $10 After, and Hermes Agent Setup

    A current guide to OpenCode Go: real 5-hour / weekly / monthly dollar caps, 18 open models, top-up behavior, then exact Hermes Agent setup.

    · evidence
  88. 88
    ai

    QwenCloud Token Plan: Lite vs Standard vs Pro, and Hermes Agent Setup

    QwenCloud Token Plan Individual compared ($6/$18/$68 limited-time): 5-hour and 7-day Credit windows, then exact Hermes Agent setup with the dedicated Token Plan API key and base URL.

    · evidence
  89. 89
    ai

    Nous Portal Subscription: Plans, Credits, and Hermes Agent Setup

    Free, Plus, Super, and Ultra tiers on Nous Portal, plus the one-command Hermes Agent setup and how to verify every tool routes through your subscription.

    · evidence
  90. 90
    ai

    Hermes Agent Provider: MiniMax M3 (Direct)

    Wire MiniMax M3 directly to Hermes Agent as the primary model: the exact provider id, model string, environment variable, setup steps, and smoke test.

    · evidence
  91. 91
    ai

    MiniMax Token Plan: Plans, Limits, and Hermes Agent Setup

    A current guide to MiniMax Plus, Max, and Ultra: real quotas and caveats, then exact setup and testing with Hermes Agent.

    · evidence
  92. 92
    policy

    Laws as Experiments: A Political System Built on the Scientific Method

    What if every law had to specify what it's trying to do, offered states a range of options to test, expired on a timer, and was evaluated by the outcomes — like a real experiment?

    · evidence
  93. 93
    ai

    Cost-aware model routing for agents

    A three-tier routing decision tree, a 15-line router, an escalation pattern with retry budget, and the cost math showing 70/20/10 split saves ~88% vs all-large.

    · evidence
  94. 94
    ai

    From JSONL to dashboard with zero infra

    Four stages with the exact tool for each: grep + awk, jq + cron, DuckDB, Metabase. Plus the graduation rule for when to upgrade and when to stop.

    · evidence
  95. 95
    computers

    GitHub Actions CI for Astro: lint, build, and deploy on every push

    Add a three-job GitHub Actions workflow that checks an Astro site, builds it, and deploys to GitHub Pages whenever main changes.

    · evidence
  96. 96
    ai

    How to find, download, and evaluate local models on Hermes Agent or OpenClaw

    A repeatable procedure for finding a model that fits your hardware, pulling it down with Ollama, and evaluating it against your own tasks on Hermes or OpenClaw.

    · evidence
  97. 97
    ai

    The 1-line observability hook that fits any agent

    A 30-line decorator that wraps every model call, writes JSONL, and answers the four questions you actually ask. The smallest hook that is useful; smaller and you fly blind.

    · evidence
  98. 98
    ai

    The four tiers of agent memory (overview)

    A shared vocabulary for memory and a decision rule for which tier to write to at any moment. Start here, then pick a tier to go deeper on.

    · evidence
  99. 99
    ai

    The three-phase context loop: grounding, messy middle, landing

    A working frame for thinking about agent prompts as three deliveries, not one — and a checklist for what context belongs in each phase.

    · evidence
  100. 100
    ai

    Tier 1 working memory: prompt hygiene beats size

    Five rules for keeping the model's context window lean — recency is not enough, compress don't copy, de-duplicate by reference, strip the system preamble, drop successful retries.

    · evidence
  101. 101
    ai

    Tier 2 session memory: the run log you actually use

    A schema for one-line-per-step JSONL run logs, a directory layout with an index, three small tools that turn the log into something you will actually open, and a what-to-log / what-not-to-log list.

    · evidence
  102. 102
    ai

    Tier 3 project memory: the file-based second brain

    Four files that an agent reads at session start — MEMORY, USER, DECISIONS, RUNBOOK — with sharp rules on what belongs in each, when to graduate to a vector DB, and what NOT to put in project memory.

    · evidence
  103. 103
    ai

    Tier 4 long-term user memory: don't be creepy, be useful

    The rule for what to remember about an operator, the audit pattern, the trimming cadence, the four failure modes, and the legal floor under GDPR right-to-erasure.

    · evidence
  104. 104
    ai

    Why every agent needs a cost cap on day one

    Three numbers, one enforcement point, and a hard ceiling before the bill lands. The cheapest pattern that separates an agent you run from one you babysit.

    · evidence
  105. 105
    ai

    Your first local-first agent on a Mac mini in 90 minutes

    Pick an agent and a local model, then walk through 90 minutes yourself or ask the agent to do most of it. Hermes Desktop path + Claude Code + Ollama wiring.

    · evidence
  106. 106
    ai

    AI Coding Is a Nightmare. You're Not the Only One — and There's a Pattern.

    The seven failure modes HN developers are reporting, what the Anthropic 2026 RCT found about AI-assisted coding, and seven interaction patterns that produce better code.

    · evidence
  107. 107
    ai

    Different Ways of Using LLMs for Coding: Eight Patterns Beyond the Prompt-Response Loop

    A field guide to the alternative coding-agent patterns developers are actually using in 2026 — hermetic agents, tab-model editors, literate programming, workboxes, containerized sessions, and more.

    · evidence
  108. 108
    ai

    SEO for AI Answer Engines in 2026: The robots.txt, llms.txt, and Content Decision

    How to be cited by ChatGPT, Perplexity, Claude, and Google's AI Overviews — the Search/Agent/Training bot taxonomy, llms.txt, and a per-goal decision matrix.

    · evidence
  109. 109
    computers

    Turn Off Ring Video Descriptions: The Fast Way (and What You're Actually Turning Off)

    A 30-second path to disable Ring's AI-generated Video Descriptions across every camera, plus the per-device path, what the feature does, and who can't see the toggle.

    · evidence
  110. 110
    ai

    Anthropic's Doom Ad and the Messenger Paradox

    Anthropic bet its brand on a 90-second film of burning houses and cemetery rows. The internet called it the best anti-AI ad ever made. What the campaign actually teaches.

    · evidence
  111. 111
    computers

    ABS Search Bar: The Cheap State Update, Not a Popup

    A single client-side script, four pages, no backend. The ABS search bar filters a JSON index already on the page — sub-50ms on every keystroke, no popup.

    · evidence
  112. 112
    ai

    AI Model Matrix Quickstart: A Five-Axis, Three-Filter Way to Pick a Model

    Use the ABS Model Matrix to pick an LLM in under two minutes: five axes, three filters, the cheapest-viable heuristic, and a worked Sonnet 4.5 / GPT-5 / M3 / Gemini 2.5 Pro example.

    · evidence
  113. 113
    computers

    Cron Job Prompts That Stay Self-Contained: The Discipline

    A Hermes cron runs in a fresh session with zero memory. Six elements make the prompt self-contained; three anti-patterns break it. Skip any and the job runs but does nothing.

    · evidence
  114. 114
    computers

    Daily Memory Audit and the 4-Step Merge Plan: From Cron Flag to Clean Memory

    Operator playbook for the nightly memory-audit cron: the 4-step merge plan (read / propose / dedupe / commit), the 3 categories of stale memory, and the 5 traps that look mergeable but are not.

    · evidence
  115. 115
    ai

    Gemini 2.5 Flash vs 2.5 Pro vs 1.5 Pro: Which One Should You Use?

    Gemini 2.5 Flash is the cheap workhorse, 2.5 Pro is the reasoning model, and 1.5 Pro is now legacy. Here is the practical capability and cost ladder.

    · evidence
  116. 116
    ai

    GPT-5 vs GPT-5 Mini: When Each Makes Sense

    A practical decision rubric for the OpenAI flagship tier vs the mini tier: per-token cost, the six capability axes, three tasks where mini is wrong, and three where it is right.

    · evidence
  117. 117
    ai

    Hermes Cron Job Authoring: The Shape and the Rules

    A practical Hermes cron playbook: jobs.json fields, supported schedule shapes, a daily-traffic example, and four checks that catch bad jobs before they run.

    · evidence
  118. 118
    computers

    Hermes Curator: The Weekly Lint Gate for Skills and Memory

    Operator playbook for hermes curator as the weekly lint gate: the 13-verb CLI surface, the 4 lint rules (orphan / stale / oversized / duplicate), and the pin-vs-prune decision.

    · evidence
  119. 119
    computers

    Hermes Memory Trim: A Proposal-Only Workflow

    Operator playbook for the weekly memory trim: the proposal-only contract (never mutate), the 4 verdicts and 5 trim candidates, the diff-and-confirm UX, and the 3 verification checks.

    · evidence
  120. 120
    ai

    Hermes Memory: What to Save and What Not To

    Eight-category rule for Hermes MEMORY.md and USER.md: four always-save kinds, four never-save kinds, the 2k-char ceiling, and the Johnny5 routing gate.

    · evidence
  121. 121
    computers

    How to Read an LLM Pricing Table Without Fooling Yourself

    A practical guide to the LLM pricing table: compare input, output, and context window first, then treat cached, batch, and free-tier prices with care.

    · evidence
  122. 122
    ai

    Local LLM with Ollama: The Easy Mode (L7)

    Ollama is the easiest path to a local LLM — three install steps, the 8B/14B/32B/70B shorthand, the q4_K_M / q8_0 / :instruct tag conventions, and the three boxes where it wins and loses.

    · evidence
  123. 123
    ai

    Set Up vLLM on a Single GPU: From Drivers to an OpenAI-Compatible Server

    Install vLLM on one NVIDIA GPU, choose a model that fits, launch its OpenAI-compatible API, and avoid startup and production memory traps.

    · evidence
  124. 124
    ai

    MiniMax M3 vs Haiku 4.5 vs Gemini 2.5 Flash vs GPT-5-mini: The Budget-Tier Map

    Four cheap LLMs compared on the only axes that matter on a budget: input, output, context, and the workloads where each one quietly wins or quietly costs you.

    · evidence
  125. 125
    ai

    Temperature, Top-P, and the Other Sampling Knobs: When to Touch Them and When to Leave Them Alone

    When to touch temperature, top_p, top_k, frequency/presence penalties, max_tokens, and seed — and the default-everything rule that keeps most calls boring.

    · evidence
  126. 126
    ai

    Provider Rate Limits and the Fallback Rule: When Hermes Must Switch vs When It Must Hold

    The three rate-limit shapes (RPM, TPM, RPD), the hermes fallback chain (anthropic → openrouter → local), when the chain fires, and the fallback must never pay twice rule.

    · evidence
  127. 127
    computers

    ABS Chat with Skills and Memory: A Practical Scoping Guide

    Run ABS chat sessions with skills, memory, and fact_store in context, then scope the load when cost, latency, or focus matters.

    · evidence
  128. 128
    computers

    ABS Cron Job Self-Contained Prompts: The Rule

    A Hermes cron prompt runs in a fresh session — no memory of past runs, no inline handoff. Six elements make it self-contained. Skip any one and the job silently returns nothing useful.

    · evidence
  129. 129
    computers

    ABS Frontmatter: Required vs Optional Fields, the Rule

    ABS frontmatter is enforced by six Zod validators in src/content.config.ts — title ≤120, description ≤200, four enums, and a URL array. Build fails loudly on violation. Defaults fill the rest.

    · evidence
  130. 130
    computers

    ABS Content Frontmatter: The Canonical Shape

    A field-by-field guide to the canonical ABS post header, including key order, image conventions, valid enums, and the Astro publishing pipeline.

    · evidence
  131. 131
    computers

    ABS Keyboard Shortcuts: n, r, b (No Prefix)

    ABS ships three single-letter shortcuts, no prefix, no modifiers, no armed state. The bindings, why we left a g-prefix design behind, what we considered and cut, and the skip-rules.

    · evidence
  132. 132
    computers

    ABS pubDate: Timezone UTC-Only, the Decision

    ABS coerces pubDate to a UTC Date via z.coerce.date() and sorts by epoch ms. Local-clock pubDates are an anti-pattern; DST swaps a post's rank twice a year. The schema, the comparator, the verify.

    · evidence
  133. 133
    computers

    ABS Mobile Nav: The Three Rules

    Mobile nav on ABS runs through a drawer (not modal), with aria-expanded state on the toggle, and focus stays trapped inside until closed. The body-scroll-lock and the close behaviors, in three rules.

    · evidence
  134. 134
    computers

    ABS Regression Suite: Eight Failure Modes and Their Cheap-First Checks

    A troubleshooting reference for the eight ABS regression scripts: what each failure looks like, the cheap-first check before anything else, and the canonical fix.

    · evidence
  135. 135
    computers

    Hermes `--resume` vs `--continue`: When Each Fits and the Stale-Session Trap

    When to use `hermes chat --resume <id>` (deterministic) vs `--continue` (convenient): the trade-off, the decision rule, and three stale-session failure shapes.

    · evidence
  136. 136
    computers

    ABS RSS and Reader-Friendly Output: The Decisions

    ABS publishes a single RSS feed at /rss.xml carrying news, reviews, and guides sorted newest first. The autodiscovery link tag, the no-JS reader-friendly HTML pages, and the verify commands.

    · evidence
  137. 137
    computers

    Hermes Terminal Output: The 50 KB Cap and the Pipe Fix

    Hermes terminal output can stop before the line you need. Here is how to recognise the 50 KB cap, reduce noisy output, and recover the missing bottom safely.

    · evidence
  138. 138
    computers

    Hermes write_file: When to Write to Which Path, the Rule

    Hermes write_file uses three buckets: /tmp/ for ephemeral scripts, /srv/abs-site/src/ for canonical in-tree files, /opt/data/ for operator scripts and backups. Includes the wrong-path failure shape.

    · evidence
  139. 139
    computers

    ABS Sort Pages: By Grade and Date, the Rules

    ABS review listings default to newest-first; the secondary key is grade. URL params (?sort=grade|oldest|new) reach a /reviews/[sort].astro. The four tie-breakers and why we don't sort by score.

    · evidence
  140. 140
    computers

    Affiliate Link Auto-Tag: On Every Publish, the Rule

    Every review publish runs an affiliate-link-rewrite pass: amazon.com URLs become amzn.to/agenticbotsit-20 tracked. Idempotent, run-at-deploy, audit-visible. Script, verify, failure shapes.

    · evidence
  141. 141
    computers

    Agents and the Critique Loop: When to Self-Review

    When an agent should self-review before surfacing, when to defer to a steelman judge in a multi-agent swarm, and the four-question self-check that catches the most common regression.

    · evidence
  142. 142
    computers

    Agents and the Deploy Gate: When They Push

    How an agent runs the ABS deploy playbook end-to-end: three-SHA check, regression gate, rsync to webroot, CF cache purge, commit, github push, ship-ready for operator review.

    · evidence
  143. 143
    computers

    Agents and Their Rules of Engagement: No Bulk Surprises

    An agent's contract with the operator: no surprise bulk actions, surface decisions, ask before irreversible changes, quiet mode for crons. Shipping 10 guides unchecked is a broken rule.

    · evidence
  144. 144
    ai

    Anthropic Provider on ABS: Quick Setup and the Cheap Mistakes

    Wiring Anthropic as the primary Hermes provider on ABS: API key, model pin, fallback chain position. Plus the 6 cheap mistakes operators make at setup that compound over months.

    · evidence
  145. 145
    computers

    Building and Deploying an Astro Static Site Behind a Cloudflare Tunnel

    An operator's recipe for taking a static Astro site from `npm run build` to a live URL behind a Cloudflare Tunnel, with rsync, edge cache purges, and the three-SHA deploy gate.

    · evidence
  146. 146
    computers

    ABS Affiliate Link Convention: Voice Note + amzn.to, Built-In Tag

    How the botsitter-review skill handles affiliate links: the voice note + amzn.to input shape, the auto-tag rewrite to agenticbotsit-20 at build, and the rules for writing tag-safe from URLs.

    · evidence
  147. 147
    ai

    Claude Sonnet vs Haiku vs Opus: When to Pick Which

    A practical decision rubric for the Anthropic Claude tiers: per-tier cost, speed, capability, and a 7-row table mapping everyday tasks to the right model.

    · evidence
  148. 148
    computers

    Cloudflare Access Policies for Internal Tools

    Put Cloudflare Access (Zero Trust) in front of an internal tool: app definition, Allow policy bound to an email domain, service-token back door for crons, and the audit log to subscribe to.

    · evidence
  149. 149
    computers

    Cloudflare Edge Cache: Purging Without Stale 404 Leaks

    How to purge CF edge cache so a deploy doesn't serve 404s to users mid-flight: ordered purge, HTML-first then assets, deployment window, and verification probes.

    · evidence
  150. 150
    computers

    Cloudflare HSTS Preload: The Hard-to-Undo Directive

    HSTS preload pins your domain into the browser-shipped list. Once on hstspreload.org, removing the entry takes 6-12 weeks to reach Chrome and Firefox. Avoid shipping it prematurely.

    · evidence
  151. 151
    computers

    Cloudflare Tunnel: Adding Multiple Hostnames Without Breaking the Others

    How to put several public hostnames on a single Cloudflare Tunnel: cloudflared tunnel route dns per hostname, ingress rule order, incremental test pattern, and the catch-all 404 fallback.

    · evidence
  152. 152
    computers

    Cloudflare Tunnel: When the Daemon Dies, How to Recover (cfOrigin;dur=122)

    Diagnose and recover a broken Cloudflare Tunnel: cloudflared is up but origin is silent, cfOrigin;dur=N ms traps, tunnel info diff, restart workflow, and what NOT to try before a restart.

    · evidence
  153. 153
    computers

    Cron Watchdogs vs Cron Monitors: When Each Fits and the Silent-Watchdog Trap

    The semantic gap between watchdogs (fix-then-alert) and monitors (alert-only), the 3 shapes where a monitor is right, and the 3 shapes where a watchdog is the only correct choice.

    · evidence
  154. 154
    computers

    First-Hand Versus Research-Only Product Reviews: The ABS Editorial Bar

    The ABS editorial bar is first-hand: did the operator actually use the product, in what setting, compared to what. Research-only reviews don't ship. The botsitter-review skill enforces the line.

    · evidence
  155. 155
    computers

    Git Commands an AI Agent Should Know

    The ~15 git commands an agent uses in 95% of work, the failure modes for each, and the discipline that keeps an agent's git history clean.

    · evidence
  156. 156
    computers

    Git Commit and Push as the Deploy Gate: The ABS Convention

    Why the live ABS site only deploys when local/origin/github are on the same commit. What this convention prevents, what it doesn't, and the post-receive hook that enforces it.

    · evidence
  157. 157
    computers

    Hermes Agent Cron Jobs: Authoring, Scheduling, and the Self-Contained Prompt Rule

    Author Hermes cron jobs that survive a fresh session: prompt self-containment, schedule patterns, dry-run, pause/resume, run-once, and the difference between agent and no_agent modes.

    · evidence
  158. 158
    computers

    Hermes Agent Gateway: When Messages Don't Arrive

    A troubleshooting reference for the Hermes messaging gateway: token/allow-list, adapter, runtime, routing — separating failures by cheap-first check order when the bot doesn't respond.

    · evidence
  159. 159
    computers

    Connecting Hermes Agent to Telegram: A First-Time Setup Guide

    Step-by-step setup of the Hermes messaging gateway for Telegram: BotFather token, allow-list, home chat ID, and proof that the first message actually arrives back in the chat.

    · evidence
  160. 160
    computers

    Hermes Agent Memory vs Skills: Where Things Belong

    A practical decision rule for the four Hermes storage surfaces — memory, skills, fact_store, session handoff — so durable preferences land in memory and procedures land in skills.

    · evidence
  161. 161
    computers

    Hermes Agent on a VPS: Hostinger, DigitalOcean, or Vultr First-Run Setup

    Step-by-step setup of Hermes Agent on a Linux VPS: SSH key, non-root user, Python 3.11, Ubuntu 22.04 base, cloudflared install, and the first 'Reply with OK' chat.

    · evidence
  162. 162
    computers

    Hermes Agent on Your Local PC: Windows, Linux, macOS, and WSL2

    How to install Hermes Agent on a workstation: native macOS, native Linux, Windows via WSL2, and what changes vs the VPS path.

    · evidence
  163. 163
    computers

    Hermes Agent Plugins: The Shape, the Register Entry Point, and How to Test Locally

    How to author a Hermes plugin: the directory layout, the register(ctx) entry point, three real plugin examples on this build, and how to test a plugin locally before installing.

    · evidence
  164. 164
    ai

    Hermes Agent Provider: Anthropic API (Claude family — Slack-bot, Computer-Use)

    Wiring Anthropic as a Hermes provider: API key from console.anthropic.com, the -latest model-pin rule, model-name quick-reference for the Claude family, and the Slack-bot and Computer-Use use cases.

    · evidence
  165. 165
    ai

    Hermes Agent Provider: Gemini (Google AI Studio, 2M-Token Context King)

    Wiring Google Gemini on Hermes: API key from Google AI Studio, the model family (2.5 Pro, 3 Flash, 3.5 Pro), the 2M-token context window as the standout strength, and the verify pattern.

    · evidence
  166. 166
    ai

    Hermes Agent Provider: Local OpenAI-Compatible (vLLM, Ollama, LM Studio)

    Wire any OpenAI-compatible local server as a Hermes provider: the --base-url pattern, three case studies, and the token-budget trade-off.

    · evidence
  167. 167
    ai

    Hermes Agent Provider: Nous Portal (Hermes-4-70B, Hermes-4-405B, First-Class)

    Wiring the first-class nous provider on Hermes v0.18.2 — hermes chat --setup nous, the Portal OAuth/API-key exchange, Hermes-4-70B and Hermes-4-405B, and how it differs from MiniMax and OpenRouter.

    · evidence
  168. 168
    ai

    Hermes Agent Provider: OpenAI and Codex Auth (GPT + Image Gen)

    Wiring OpenAI on Hermes: API key path (per-token), Codex OAuth path (subscription-based, drives image gen too), dashboard-vs-realtime usage trap, 14-day OAuth refresh.

    · evidence
  169. 169
    ai

    Hermes Agent Provider: OpenRouter for Cross-Vendor A/B Tests (and as a Cost Audit)

    When and how to wire OpenRouter as a fallback into Hermes: model strings, cost audit use case, fallback chain position. OpenRouter is a cross-vendor search and budget audit, not a primary.

    · evidence
  170. 170
    computers

    Hermes Agent: Switching Providers Mid-Run When a Provider Fails

    How to detect and recover from a mid-task provider failure (Anthropic 529, OpenAI 429, local Ollama OOM): the symptom per provider, the runtime fallback, and the override path.

    · evidence
  171. 171
    computers

    Hermes Agent Sessions: Listing, Searching, Resuming, and Continuing

    How Hermes persists sessions: what `hermes sessions list --source` shows, when to use --resume vs --continue, and how to grep old sessions via the SQLite store.

    · evidence
  172. 172
    ai

    Picking a Model Provider for Hermes Agent: A First-Run Decision Guide

    How to choose between Nous Portal, OpenRouter, Anthropic, OpenAI, Gemini, MiniMax, and local OpenAI-compatible endpoints for Hermes Agent. Compares auth paths, costs, and capability tradeoffs.

    · evidence
  173. 173
    computers

    Hermes Agent Skills: Authoring, Curating, and the Frontmatter Rules

    Where skills live, the SKILL.md frontmatter fields, the cross_profile flag and pinning, when to update vs add a new skill, and how curator catches drift in the weekly sweep.

    · evidence
  174. 174
    computers

    Hermes Agent: Updating Without Breaking Running Crons or the Live Gateway

    How to bring upstream Hermes commits in safely: the 10 active jobs to inventory, the pre-update disable list, the post-update smoke-test pattern, and the rollback rule.

    · evidence
  175. 175
    computers

    Hermes Agent Tools: Enabling Only What You Need

    A practical guide to Hermes' toolsets: how to list them, enable what you actually use, scope per-call via --toolsets, and avoid loading the full platform surface by default.

    · evidence
  176. 176
    computers

    Hermes Agent CLI Tools: List and What Each One Does

    The Hermes Agent CLI surface today: chat, config, sessions list --source, cron, skills, plugins, memory, fact_store, curator. The --toolsets flag for per-call scoped tool loading.

    · evidence
  177. 177
    computers

    Hermes Context Window: Budgeting for Long Sessions

    How to keep a long Hermes session productive: per-tool, per-skill, per-conversation-history budgets. What to keep, what to compress, what to drop. Operator discipline for long sessions.

    · evidence
  178. 178
    computers

    Hermes Agent on a Mac mini M4 (or other Apple Silicon Macs)

    How to install Hermes Agent on macOS: Homebrew for Python 3.11, Xcode CLI tools, launchd for cron, Ollama for local inference, and Apple Silicon-specific gotchas.

    · evidence
  179. 179
    computers

    Hermes Output: Streaming vs Final Blocks, When to Pick Which

    Hermes output arrives two ways: streamed token-by-token, or as a single final block. Each fits different surfaces — CLI, Telegram, mobile push, automation logs. The matrix of when to pick which.

    · evidence
  180. 180
    computers

    Hermes Protocol: The System Prompt, the Context Window, and the Rule Boundary

    What lives in the system prompt vs in the context window vs in user-side memory: how Hermes treats the instruction hierarchy, what travels where, and what the operator's contract actually is.

    · evidence
  181. 181
    computers

    Hermes Skills vs Memory vs Fact Store: The Storage Decision

    Three storage surfaces, three jobs. Use this duration / reuse / scope rubric to put a fact in the right place, plus the bad-shape markers that tell you the rubric is failing.

    · evidence
  182. 182
    computers

    How an AI Agent Reads a Multi-Message Input Batch

    When the operator sends multiple messages at once, the agent concatenates them into context with timestamps. A multi-message batch is one turn, not a multi-turn dialogue. The P43 rule.

    · evidence
  183. 183
    computers

    How an AI Agent Reads Your Memory on the Next Turn

    Memory is loaded at session start, not mid-session; conversation history is in-memory and dies with the session. The full disk-to-context read path and the curation discipline that keeps memory clean.

    · evidence
  184. 184
    gadgets

    Image Style: The Retro-Robot Motif (Locked Style Block)

    The locked prompt prefix behind every ABS featured illustration: crimson + electric blue on warm cream, comic-book outlines, friendly retro robot, 16:9, no text. The canonical style block.

    · evidence
  185. 185
    computers

    Image Gen Failover Chain: In Priority Order

    When the openai plugin is degraded, ABS failover chain is: openai (real key) → cached PNG set → cache-bust query string → text-only social card. Each step's trigger and verify.

    · evidence
  186. 186
    computers

    Image Gen: OpenAI Backend (Real Key, Not Codex OAuth)

    The working image-gen backend for ABS: the openai Python package driven by OPENAI_API_KEY, the model gpt-image-2-medium, ~$0.04 per 1024x1024 image, and why openai-codex OAuth is broken.

    · evidence
  187. 187
    ai

    OpenAI Provider on ABS: Quick Setup, API Key vs Codex OAuth

    Wiring OpenAI on Hermes Agent: the API key path (paid per-token), the Codex OAuth path (subscription-based, also drives image gen), when to pick which, and the dashboard-vs-realtime usage trap.

    · evidence
  188. 188
    computers

    Pause and Resume Hermes Cron Jobs Without Deleting History

    Why `hermes cron pause <id>` and `hermes cron resume <id>` beat `remove` for three operator cases. `paused_at` is added; prompt and history are preserved. Verified against 35 paused jobs on this VPS.

    · evidence
  189. 189
    ai

    Picking the Default LLM for the Botsitter-Review Skill

    How to pick the default LLM for the botsitter-review skill: a four-axis decision (cost, latency, instruction following, context), per-skill alternates, and the live AI Model Matrix as the price list.

    · evidence
  190. 190
    computers

    Publishing a Product Review on Agentic Bot Sitter, End-to-End

    The complete review-publishing loop on ABS: voice note + amzn.to → draft → image gen → WebP → build → rsync → CF purge → commit → push → three-SHA → live.

    · evidence
  191. 191
    computers

    Python Venv on This VPS: The Conventions

    The canonical Python venv on this VPS is /opt/hermes/.venv/ with Python 3.11. Library surface today: openai 2.33.0, requests, pillow, ruff. Why we never call system python3 for agent scripts.

    · evidence
  192. 192
    ai

    Review Publishing Pipeline: The Voice Note to Post Chain

    The seven-stage review-publishing pipeline: voice note + amzn.to URL in, post out. Capture, STT, first-pass draft, review pass, image gen, commit, ship. Each stage's failure shape and verify.

    · evidence
  193. 193
    computers

    Static Site Deploy: rsync, Cloudflare Purge, CDN Cache — The 3-Step Loop

    The static-site deploy loop and what each step does: rsync dist/ to the webroot, ordered CF purge (HTML -> ASSETS -> BUNDLE), then verify. Plus failure shapes and the order that minimizes 404 leaks.

    · evidence
  194. 194
    computers

    Search the Codebase Before Fixing Anything

    Before fixing any bug, search the codebase for prior attempts, related patterns, and existing utilities. The agent's most expensive mistake is fixing the wrong layer. A 4-step search workflow.

    · evidence
  195. 195
    computers

    site-watchdog-sweep: Every 5min, the Consolidation Story

    Why one Python sweeper replaced five separate 5min watchdogs, what its six checks actually probe, and how it stays silent when the operator surface is healthy and only shouts when something is red.

    · evidence
  196. 196
    computers

    Terminal Commands Versus Python: When an Agent Should Write a Script

    When to use one-liner terminal commands and when to write a 5-line Python script. The trade-off is readability and verifiability, not tool sophistication. Five signals that tip the choice.

    · evidence
  197. 197
    computers

    Terminal Commands Versus Python: When an Agent Should Write a Script

    When to use one-liner terminal commands and when to write a 5-line Python script. The trade-off is readability and verifiability, not tool sophistication. Five signals that tip the choice.

    · evidence
  198. 198
    computers

    Three-SHA Check: Asserting local == origin == github == the Commit Going Live

    The bash assertion that runs before every ABS deploy: Local and origin and github must all carry the same commit SHA as the change about to ship. Failure shapes and the auto-repair pattern for each.

    · evidence
  199. 199
    computers

    Tmp Folder vs Write_file: The Rule

    When the agent writes to /tmp/ vs when to use write_file: long scripts (>300 lines) go to /tmp/, short content uses write_file. The bash heredoc trap and the agent-tool boundary.

    · evidence
  200. 200
    computers

    Voice Note to Published Review: The Input, the Pipeline, the Output

    From a 60-second voice memo to a live ABS review: transcription, draft, image, build, deploy. What arrives in the voice note, what comes out the other side, what the operator does in between.

    · evidence
  201. 201
    computers

    WebP and PNG on the Same Deploy: The P42 Rule

    P42: every ABS featured image deploys as both PNG and WebP in the same cycle. CF serves WebP via Accept-header; the PNG is canonical. The script, the 4h cache quirk, and the verify pattern.

    · evidence
  202. 202
    computers

    WebP Companion Images for Static Sites: A Sibling PNG+WebP Pattern

    How to ship a PNG and a WebP companion for every static-site image with Pillow's quality=82, method=4, plus the <picture> helper that lets browsers pick the lighter file.

    · evidence
  203. 203
    computers

    Cloudflare Tunnel Setup for a Static Site Without Opening Any Ports

    Setup guide for installing cloudflared, creating a named tunnel, routing a hostname to it, and verifying the public hostname reaches your local origin over the tunnel.

    · evidence
  204. 204
    computers

    How to troubleshoot a Cloudflare Tunnel that is not serving your site

    Troubleshooting guide for separating local-origin, connector, DNS, and policy-layer failures in a named Cloudflare Tunnel. Cheap-first diagnostic order so you can tell which layer is broken.

    · evidence
  205. 205
    computers

    How to Set Up Hermes Agent Without Turning Everything On at Once

    Setup guide for Hermes Agent: install the CLI, prove a plain chat works, then layer on tools, memory, skills, and messaging only after the base is healthy.

    · evidence

Drafts under review

New guide formats and major revisions appear here before final publication. Each draft includes its sources and independent claim review.

Earlier drafts

These older drafts are kept as historical reference. The current versions of each guide are linked above. Each earlier draft includes a source-notes page with the original claim audit.