Context
Select, retrieve, compress, and isolate the information a model needs without treating a huge prompt as architecture.
FG / 03 / Field guide
AI
Learn through useful work: bounded authority, visible failures, repeatable evaluations, and a real quality bar.
Pulse
Current briefing
What is moving now.
01
Get your bearings
Separate models, products, workflows, agents, and the harness that makes them dependable. A model demo is not a production system, and an agent is not merely a longer prompt. Reliability lives in the surrounding constraints and feedback loops.
02
Start here / Three moves
- 01
Choose a repeated job
Define inputs, output artifact, quality bar, privacy class, unacceptable failures, time saved, and the human decision that remains.
- 02
Build the smallest loop
Use explicit instructions, structured output, minimal tools, permission boundaries, logs, and a clear stop condition.
- 03
Keep an evaluation set
Save representative examples, known traps, latency, cost, tool failures, and review outcomes; rerun them after changes.
03
Build capability
Harness
Control tools, permissions, state, retries, handoffs, tracing, budgets, and durable work products.
Evaluation
Measure actual workflow quality with examples, graders, failure taxonomies, human review, and regression checks.
04
Choose gear / Decision rule
Choose models and platforms by the actual workflow: data sensitivity, quality, tool use, modalities, latency, cost, maintenance, auditability, retention, access control, and portability. Benchmark the work—not the launch demo.
before
affiliate
05
Your source map
Start with voices you already value. Verify before concluding.
YouTube
9Podcast
1- AI Daily BriefPodcast ↗
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- The Wiz AIInstagram ↗
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- Evolving AIInstagram ↗
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- Aria CodezInstagram ↗
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- God of PromptInstagram ↗
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- Setups AIInstagram ↗
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- Setup SpawnInstagram ↗
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Newsletter
7App / Tool
5- Hermes AgentApp / Tool
- Grok / Grok BotApp / Tool
- Claude CodeApp / Tool
- CodexApp / Tool
- DGX Spark / StationApp / Tool
Reference layer
Use these to settle claims—not to manufacture certainty.
Field Notes
Original editorial work
Independent analysis built from the strongest available evidence.
Field NotePublished
The Self-Hosted Software Audit: When to Fire SaaS and Run Your Own Tools
Replacing monthly subscriptions with local and self-hosted open-source software can restore privacy and eliminate recurring fees, but only if you are willing to pay the maintenance tax.
Read the working note →Field NotePublished
The Deputization Audit: How to Hand Off Work Without Losing Your Grip
When software can record your keystrokes and study your habits, the real problem is no longer what AI can do—it is deciding what you should never let it touch.
Read the working note →Field NotePublished
Running 224 GB of Split VRAM: The Reality of Mixing AMD APUs and NVIDIA eGPUs
Bridging an AMD Strix Halo mini-PC to a high-end NVIDIA card over OCuLink proves cross-vendor local inference works, but the practical friction and cost are steep.
Read the working note →Field NotePublished
Clustering Mini Supercomputers: The Real Math Behind 512GB Local VRAM
Stacking four NVIDIA DGX Spark boxes unlocks half a terabyte of unified memory, but near-linear scaling cannot hide the brutal cost-per-token reality of desktop clusters.
Read the working note →Field NotePublished
Curing the Machine Stench: How to Strip the Tics from AI Writing
Large language models default to a recognizable brand of breathless, smug prose. Fixing it requires persistent negative constraints rather than reactive prompting.
Read the working note →Field NotePublished
Breaking the Chatbox Habit: A Practical Filter for Emerging AI Workflows
Most people locked in their AI habits two years ago. Cutting through the feature bloat reveals which newer techniques actually improve your work, protect your privacy, or waste your time.
Read the working note →Field NotePublished
Agentic Workflows vs Foundation Models: Distribution and Execution in Practice
As frontier AI benchmarks plateau, enterprise value is shifting from raw parameter reasoning to the messy reality of multi-step agent execution, security boundaries, and platform distribution.
Read the working note →Use Cases
Applied AI
Concrete jobs where AI earns its place: the workflow, the human boundary, the failure modes, and what success looks like.
Use CaseIdea
Continuous Market and Regulatory Intelligence via Persistent Agent Workspaces
How to deploy always-on background agents that aggregate competitor moves and policy shifts across tools without constant re-prompting.
Open use case →Use CaseIdea
Deploying Specialized Judgment Models as Low-Cost Verification Gates in Agent Workflows
How to intercept agentic tool calls with fast, non-generative judgment models to slash latency, prevent runaway API spend, and block malformed actions before execution.
Open use case →