$2,200 SETUP. $1,500 A MONTH. 6 CLIENTS PAID. HE'S BUILDING ROLE-SPECIFIC AI BRAINS WHILE THEIR COMPETITORS SHARE ONE CHATGPT LOGIN.
No SaaS. No wrapper. No API resale.
Most companies right now: one shared ChatGPT login, everyone dumping questions into the same chat.
His version: one vault per company. Every employee opens the chat and it already knows their role, projects, and access level.
At 0:30 the employee profile is right there — role, permissions, active projects. That's the layer OpenAI doesn't sell.
His loop:
> Pulls 90 days of client comms into one vault
> Splits by department and role
> Every employee opens a chat pre-loaded with their own context
$2,200 setup. $1,500/mo. 6 clients. $9,000/month.
Most companies still share one login. He's already on client seven.
Save the loop. The tools change. The model doesn't.
Would you keep sharing one ChatGPT login — or ship the version each person opens with their own brain already loaded?
TWO GOLD NVIDIA BOXES ON A DESK JUST PUT A 30B CODER MODEL AT 2107 TOKENS PER SECOND — AND EVERY AI SUBSCRIPTION STACK IN THE ROOM STARTED LOOKING EXPENSIVE.
The terminal screen shows three numbers side by side. M4 Pro Mac Mini: pp512 = 563. Strix Halo: pp512 = 342. DGX Spark: pp512 = 2107.
That is prompt processing — the phase where the machine digests your entire context window before writing a single token back. Token generation across all three sits between 55 and 83. Roughly the same. But prefill on the Spark is 3.7x faster than the Mac Mini and 6x faster than Strix Halo.
The model is Qwen3-Coder-30B-A3B-Instruct, Q4 quantization, run locally via llama.cpp bench. A 30B coder model.
For solo inference on short prompts, the gap barely matters. For overnight Claude Code agents, 50k-token RAG pipelines, or multi-file codebase runs — prefill is the actual bottleneck, and 2107 t/s stops feeling theoretical.
DGX Spark runs on a GB10 Grace Blackwell chip with 128 GB unified GPU memory. That memory bandwidth is where the gap comes from. The Mac Mini M4 Pro has 64 GB unified memory and solid Apple Silicon throughput — different architecture class. Strix Halo is AMD's unified memory answer on x86, fast for the price, but the PP ceiling shows.
The caveat that earns its place: DGX Spark starts around $3,000. It pulls real wattage, runs a full CUDA stack, and needs more physical space than a shelf box. For occasional inference, the Mac Mini is still the rational call.
But for anyone running agents overnight or processing private codebases where context is always long — the prefill gap stops being a benchmark curiosity. The two gold boxes on that desk are not a homelab flex. They are a different category of local inference machine.
THE CEO OF ANTHROPIC SAYS HE DOES NOT PROMPT CLAUDE ANYMORE. HE WRITES LOOPS.
For the last few years, the valuable AI skill was prompt engineering. You wrote better instructions, got better outputs, then kept babysitting the agent through the next step.
That workflow is starting to look old. The new skill is building systems that prompt the agent for you.
A loop takes one goal and keeps moving. It finds the work, hands it to the agent, checks the result, remembers what finished, and runs the next step until the job is actually done.
The detail most people miss: loop engineering is not one magic script. It is a stack of control layers around the model.
Automation kicks it off. Worktrees let multiple agents work without colliding. Skills keep project context reusable. Connectors let the system touch real tools. Sub-agents separate the builder from the reviewer.
That is when Claude Code stops feeling like a chatbot. It starts feeling like a small team of employees working through a queue while you are not there.
Most people are still trying to write better prompts. The leverage is moving to writing the loops that make prompting happen without you.
A HONG KONG UNIVERSITY JUST OPEN-SOURCED AN AI HEDGE FUND THAT RUNS ON YOUR LAPTOP.
It is called Vibe Trading. You type what you want in plain English, and a team of AI agents turns it into a trading workflow.
One agent researches. One builds the strategy. One runs the backtest. Another reviews the risk before anything touches real money.
The detail most people miss: this is not just a trading bot. It is a full quant desk compressed into a terminal.
It pulls live data from 18 sources across stocks, crypto, and forex, then stress tests strategies with Monte Carlo instead of letting vibes drive the trade.
The most useful feature is not even the strategy builder. Upload your broker history, and it can show which of your own habits are quietly bleeding money.
400+ prebuilt quant strategies. 15,000 developers. Open source and free.
Most people use AI to ask “what should I buy?” The better use case is building a system that researches, tests, audits, and exposes bad decisions before they become expensive.
This man just built a clean white local AI powerhouse.
For his machine he used the ROG Strix RTX 5090, AMD Ryzen 9 processor, 64GB+ high-speed RAM, ROG Glacial AIO liquid cooler, ROG Thor 1200W Platinum PSU, multiple ROG components, Crosshair motherboard, and the premium ROG Citadel white case.
The wins:
- Monster on-device performance for running large models and agents locally
- Blazing fast inference with zero cloud latency or costs
- Full privacy and control over data, RAG systems, and private workflows
- Premium cooling and power delivery for sustained heavy loads
- Future-proof setup that eliminates hourly API bills and rate limits
The pain: Noise like a jet engine. Heat turns the garage into a sauna. One PSU failure and the whole rig goes down.
But it steal worth this.
No more rented compute. Pure ownership on the desk. Build what matters next.
your best prompts already exist,
they're just buried in your chat history,
you scroll back, find the good one,
paste it, lose it again by tomorrow,
the move isn't writing better ones,
it's promoting the ones that already work into a file
save it once as a SKILL. md
it fires itself, exactly when the task fits,
most people are sitting on ten of these and never notice
Chatbots. Voice assistants. Educational apps.
AI is shaping how children grow, learn and interact every day. While it brings exciting opportunities, it also comes with real concerns.
We’ve put together 5 simple tips to help parents navigate it all.
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[PÉTITION] Demandons à Mix Buffet de mettre fin aux pires pratiques d’élevage en s’engageant à respecter le Pig Minimum Standards https://t.co/WiKLDRWpCL
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J’ai une demande concrète et urgente à faire au nouveau Premier ministre @MichelBarnier :
L'arrestation de Paul Watson est injuste et injustifiée. pouvez-vous vous engager officiellement pour sa libération ?
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Le Perche…
Ses belles maisons normandes 🏡
Ses villages typiques 🏞️
Sa nature préservée 🌳
Et son élevage intensif LDC (Le Gaulois, Maître CoQ) en roue-libre.
@Prefet61 dites STOP ! 👇
https://t.co/qwIK14MyKL
Bonjour @MFesneau@Agri_Gouv@Prefet53@guebriant, qu’attendez-vous pour prendre vos responsabilités suite à la nouvelle enquête de @L214 et fermer l’abattoir de Craon en Mayenne où des animaux sont découpés vivants ? ⤵️
https://t.co/xAwMFPjP0J
Cette enquête de @L214 est insoutenable! À l’abattoir de Bazas (33), la majorité des animaux sont issus d'élevages en plein air, locaux ou bio. Sous les yeux des vétérinaires impassibles, les animaux vivent l’horreur. Demandez sa fermeture immédiate https://t.co/o0dQRw7rs0
Aidez à stopper le projet du premier élevage de pieuvres au monde à Grande Canarie. Ces êtres intelligents connus pour utiliser des outils et décorer leurs maisons ne devraient pas être confinés dans des bassins surpeuplés ! https://t.co/f0RUwv7ELR