Several products compared line by line, with a clear recommendation at the end.
Use a public cloud-planner/local-worker experiment to compare both delegation directions, retain failures and rework, and measure costs against the same acceptance criteria.
Jev is the "System One model" TypeSafe AI released on September 15, 2026: it generates no text and only picks from options you define in advance, returning probabilities. Using the official docs, this article explains how it works, which decisions it suits, and the weaknesses the vendor itself lists. It then checks the marketing numbers against independent tests, and closes with steps for trying it on a single decision point and a tiered setup that pairs it with LLMs.
Evaluate candidates on real tasks, check context, latency and maintenance on the target hardware, then use genuinely cancelable monthly spending to decide whether to buy.
More desktop agents can read your files, click buttons and send messages for you, yet their permissions are usually granted by clicking "Allow" all the way through a setup wizard. This article splits what an agent can reach into four layers: system, app, accounts and outside actions. It then walks through six steps: how to isolate before installing, how to grant system permissions one at a time, how to check the agent's own switches, which actions you must confirm yourself and how to take access back afterward, plus separate notes on configuring coding agents.
9Router is an AI coding router that runs on your own machine, gathering subscriptions, low-cost APIs and free tiers behind a single OpenAI-compatible endpoint. This is the full walkthrough after the one-line npm install: what to do about the default password of 123456, what the Providers and CLI Tools pages each control, how to configure Docker and DATA_DIR, and which features are best left alone.
A full record of bringing LiteLLM 1.99.0 up from scratch on a laptop: the smallest config that works, how to verify it, deliberately breaking the primary model to watch fallback fire, issuing virtual keys with budgets from the admin UI, and the traps you only hit by actually installing it — such as prisma still being missing after pip install 'litellm[proxy]'.
Sub2API is an open-source gateway that turns Claude, OpenAI and Gemini subscriptions into a single API entry point. This is a full v0.2.0 install from scratch: the Docker Compose and binary routes, the four-step setup wizard, the compliance phrase you must type before the console opens, how the account pool and groups fit together, and why the author put a ban warning in the first paragraph of the README.
When you have twenty or thirty PDFs to get through, what decides the outcome is triage and a fixed field template, not which model you pick. This playbook gives seven steps: classify the documents, handle scans, lock the extraction fields and require a page number on every line, then spot-check for accuracy, consolidate into a table and hunt for contradictions. Includes copy-ready prompts, a material checklist, time and cost estimates, and seven common ways it misreads.
Channels rarely die because writing is slow; they die because the topic pipeline runs dry and publishing stops. This playbook breaks "run a channel with AI" into eight steps: define the columns, build a topic bank, then run one repeatable per-issue pipeline — topic, outline, draft, human rewrite, visuals, headline, publish, review. Includes copy-ready prompts, a material checklist, per-issue time and monthly cost estimates, and the seven ways an account dies.
Breaks "make a video with AI" into nine steps you can follow: settle the one-line conclusion and your sources, write a script that reads aloud cleanly, turn that script into a shot list with durations, then take it into the editor for sync, subtitle proofing and a cover. Includes copy-ready prompts, a material checklist, first-video versus practised timings, the cost structure, and the seven most common ways it goes wrong.
Knowledge base projects rarely fail because the model is weak; they fail because the documents are dirty. This playbook gives a workable path: write down the questions it must answer first, then filter documents, preprocess them, standardise their headers, and get it running on NotebookLM or your team's document platform before considering a self-hosted RAG stack. Includes how to build an evaluation set, copy-ready prompts, time and cost estimates, and seven typical failures.
For creators and teams that already run a stable program: split ChatGPT, Claude, Gemini, Midjourney, Runway, ElevenLabs, and n8n across research, editing, visual, audio, and assembly roles to build a content pipeline with sources, versions, budgets, and a human final review, then feed performance data back into the program.
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