AI Guides
124 AI guides. Weight: evidence — the work was tested, the steps are real.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 11 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.
- 12 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.
- 13 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.
- 14 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.
- 15 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.
- 16 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.
- 17 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.
- 18 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.
- 19 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.
- 20 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.
- 21 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.
- 22 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.
- 23 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.
- 24 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.
- 25 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.
- 26 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.
- 27 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.
- 28 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.
- 29 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.
- 30 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.
- 31 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.
- 32 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.
- 33 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.
- 34 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.
- 35 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.
- 36 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.
- 37 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.
- 38 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.
- 39 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.
- 40 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.
- 41 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.
- 42 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.
- 43 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.
- 44 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.
- 45 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.
- 46 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.
- 47 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.
- 48 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.
- 49 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.
- 50 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.
- 51 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.
- 52 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.
- 53 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.
- 54 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.
- 55 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.
- 56 ai
The Five Engineering Disciplines Behind Every AI Agent
The five engineering disciplines behind every AI agent, and when to reach for each.
- 57 ai
Context Engineering
Context engineering: curating exactly what an AI agent sees, the discipline named in mid-2025.
- 58 ai
Graph Engineering
Graph engineering: wiring many agents into one team, and the origin credit it actually deserves.
- 59 ai
Harness Engineering
Harness engineering: the invisible scaffolding built around a model that makes an agent work.
- 60 ai
Loop Engineering
Loop engineering: designing the observe-plan-act-check-repeat cycle an AI agent runs.
- 61 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.
- 62 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.
- 63 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.
- 64 ai
Kimi K3
Kimi K3 (Moonshot AI, July 27, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.
- 65 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.
- 66 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.
- 67 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.
- 68 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.
- 69 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.
- 70 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.
- 71 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.
- 72 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.
- 73 ai
Solar Pro 4
Solar Pro 4 (Upstage, 2026): what the release is, why it matters for operators, specs, benchmarks, and the call.
- 74 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.
- 75 ai
Prompt Engineering
What prompt engineering is in 2026, how to do it, and when it stops being enough.
- 76 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.
- 77 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.
- 78 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.
- 79 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.
- 80 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.
- 81 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.
- 82 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.
- 83 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.
- 84 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.
- 85 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.
- 86 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.
- 87 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.
- 88 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.
- 89 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.
- 90 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.
- 91 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.
- 92 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.
- 93 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.
- 94 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.
- 95 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.
- 96 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.
- 97 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.
- 98 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.
- 99 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.
- 100 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.
- 101 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.
- 102 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.
- 103 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.
- 104 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.
- 105 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.
- 106 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.
- 107 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.
- 108 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.
- 109 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.
- 110 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.
- 111 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.
- 112 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.
- 113 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.
- 114 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.
- 115 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.
- 116 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.
- 117 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.
- 118 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.
- 119 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.
- 120 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.
- 121 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.
- 122 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.
- 123 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.
- 124 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.
