The jusCode blog
Stop worrying about models. Read about the work.
Practical notes on coding agents, model-agnostic inference, and shipping software you can prove.
The 11 Layers of the AI Inference Stack: From Prompt to Silicon
Inference stack means something different to everyone. Follow one request from the app to the silicon and it resolves into 11 layers: gateway, router, orchestrator, cache, serving, inference engine and more.
Why We Launched jusCode Academy: Closing the AI Skill Gap with Role-Based Certifications and a Practice Arena
The AI skill gap in open data: IMF says 40 percent of jobs are exposed, WEF says 63 percent of employers are blocked by missing skills, and capability doubles every 7 months. Why we built jusCode Academy, and the first Loop Engineering Certification and Practice Arena.
The Task Economy: Inside the Trillion-Dollar Market Where Work Becomes a SKU
The unit of work is shrinking from jobs to gigs to tasks, and a spot market for work is forming around AI agents. We size the task economy from open data (IMF, ILO, O*NET, METR, GDPval), map its anatomy, pricing physics, and trust stack, and show who captures the trillion.
Follow the Dollar: Unit Economics of the AI Inference Stack, and Where Value Capture Moves Next
We dissected one dollar of AI spend across the 11 layers of the inference stack: who captures value today, why power belongs to whoever holds attention and outcomes, why model labs are storming the application layer, and what payment networks, app stores, and ad tech predict about tomorrow's tolls.
jusTokenMax: Cut Coding-Agent Token Costs Before the Model Sees Them
jusTokenMax is an open-source token-reduction toolkit for coding agents. It compresses heavy inputs before they reach the context window: PDFs to Markdown, logs and JSON digested, lockfiles collapsed, whole-file reads replaced by symbol lookups. Measured 56 to 99 percent reductions, fully reversible.
The 8 Layers of the RAG Pipeline: Why Retrieval, Not Your Model, Decides Answer Quality
A layered breakdown of the RAG pipeline: ingestion, chunking, indexing, retrieval, reranking, context assembly, generation, and evaluation. Plus the Yield Chain: why eight layers at 90 percent give you a 43 percent system, and why swapping models rarely fixes it.
Loop Engineer: The Top Job of 2026 H2, and the Job Description Nobody Has Written
Loop engineer is becoming the job title of 2026 H2. Here is the actual job description: the skills ladder from prompt to loop engineering, why this title sticks where prompt engineer faded, the management skills that transfer, and a four-week plan to become one.
Loop Engineering for CXOs: What Changes When Engineers Stop Prompting and Start Designing Loops
Loop engineering explained for executives: why AI agent loops turn engineering from a labor line into a capacity line, the governance inversion nobody briefs the board on, the five questions to ask your team, and a 90-day adoption plan.
Welcome to the jusCode blog
Notes on coding agents, model-agnostic inference, and shipping software you can prove.
The Cost of a Loop: Token Economics of AI Agents That Run While You Sleep
The real token economics of agent loops: why accumulating context makes loop cost grow quadratically, the worked math from $25 a run down to $2.25, and the three numbers to put on your dashboard before your agents run overnight.
Loops Are Layer 04: Where Loop Engineering Fits in the AI Inference Stack
Loop engineering is not just a workflow trick. Loops are machine customers of your inference stack: one intention becomes fifty model calls, and every layer from gateway to GPU feels it. Here is what changes, and why loop traffic is the most cacheable traffic you have.
The Anatomy of an Agent Loop: Trigger, Goal, Gate, and the Stop Condition It Can't Fake
Loop engineering explained from the inside: the five organs of an agent loop (trigger, goal, gate, memory, budget) and the Dumb Verifier Principle that separates loops that ship from loops that spin.
How to design an AI chat assistant like ChatGPT, JusInfer.Chat and others
The two system design patterns behind every AI chat assistant: real-time token streaming and managing long-running work on scarce GPUs.
Gift a future: give a certification, give an AI education
You can now gift a jusCode certification or jusCode credits to a friend's email. Give someone proof of their skills, or the runway to practice with AI agents. Here's how it works.
Hiring AI-fluent roles via hackathon: why hands-on assessment beats a résumé in 2026
AI tools collapsed the gap between average and excellent for product managers, business analysts, QA, ops, and consultants. A 4-hour task tells you more than a 4-round interview loop. Here's how to run one, and where it breaks.
The inference chokepoint of 2026
The bottleneck in AI coding is no longer model quality. It's getting tokens out at a price that lets your agent run all day. A thesis on where the real chokepoints are in 2026 inference economics, and the three layers where you can still find leverage.
Codex CLI custom providers: what works, what's tricky, and what's coming
A practical walkthrough of OpenAI Codex CLI's custom provider config: the TOML format, the Responses-API requirement that trips everyone up, and where jusCode fits.
Open Interpreter + custom provider: cheap inference for a code-running agent
Open Interpreter runs code on your machine and needs a model behind it. With one --api_base flag you can route through jusCode and drop inference cost 60-80% without changing how OI runs code.
Roo Code + custom endpoint: agent forks shouldn't cost frontier prices
Roo Code (a Cline fork) supports any OpenAI-compatible base URL. Here's the 2-minute config that routes through jusCode and drops autonomous-edit bills 60-80% without giving up Roo's multi-mode workflow.
LLM gateway comparison 2026: OpenRouter vs Helicone vs Portkey vs jusCode
There are four common shapes of LLM gateway in 2026. They look interchangeable from the API surface and aren't. Here's what each is actually optimized for and when to pick which.
Use Goose (Block) with a custom provider: 5-minute setup
Block's open-source Goose agent toolkit accepts any OpenAI-compatible provider. Here's how to route it through jusCode for cheaper, model-agnostic coding without changing your goose-extensions or workflow.
Aider on a budget: point it at a cheap OpenAI-compatible endpoint
Aider is one of the most token-hungry coding agents because it sends full file context every turn. Here's how to cut your Aider bill 70% with one environment variable.
Inference endpoints for coding agents: what's actually different
A coding-agent inference endpoint isn't just a chat endpoint with longer context. It has different latency profile, different tool-use semantics, different caching needs. Here's what to look for.
The cheapest LLM API for coding agents in 2026, ranked
Honest cost-per-1k-tokens comparison across OpenAI, Anthropic, Together, Fireworks, OpenRouter, and jusCode for typical coding-agent workloads. Updated May 2026.
Hermes models for coding agents: what they're good at, what they're not
Hermes 3 and Hermes-style instruction-tuned models punch above their weight on tool use. Here's where they fit in a coding-agent stack and how to route them via an OpenAI-compatible endpoint.
Together vs Fireworks vs jusCode: pick the right open-weights gateway in 2026
All three host open-weights models behind OpenAI-compatible endpoints, but they serve different jobs. Honest side-by-side: catalog, latency, per-token rates, routing logic, and when to pick each.
OpenRouter alternatives in 2026: when to use each
Side-by-side review of OpenRouter, Portkey, LiteLLM, Helicone, and jusCode. When each makes sense, what they cost, and the architectural tradeoffs.
OpenAI-compatible API, explained: what it actually means in 2026
Half the LLM ecosystem advertises "OpenAI-compatible." Some compatibility is real; some is shallow. This post explains what the term means, what to test before trusting it, and why drop-in compatibility is the most important standard in AI infrastructure.
What is an inference endpoint? A 2026 guide for AI builders
Plain-English explanation of inference endpoints, how they differ from training, what OpenAI-compatible means, and how to choose one for your application or coding agent.
Cline + custom endpoint: cut your VS Code agent bill 60%+
Cline supports any OpenAI-compatible base URL via its "OpenAI Compatible" provider. Here's the 2-minute config that drops your autonomous-edit bill 60-80% without changing how you use it.
Your Cursor bill is too high: three ways to cut it in 2026
Cursor's default settings push every keystroke to Sonnet 4.5. Here are three concrete ways to drop the monthly bill 50–80% without changing your workflow, ranked by effort.
Your custom agent harness: point it at a cheaper endpoint in one line
Building your own coding-agent harness (OpenClaw, nemoClaw, Hermes-based, in-house)? If it speaks OpenAI Chat Completions, it speaks jusCode. Here's the universal config plus what to watch for.
AI coding tools in 2026: 12 picks ranked by what they're actually good at
Honest ranking of the AI coding tools worth using in 2026, organized by use case. Plus where each fits in a real engineering team's workflow.
Continue.dev: configure a custom model in 30 seconds
Continue (VS Code + JetBrains) is the most flexible open-source coding agent. Here's the config.json snippet that points it at any OpenAI-compatible endpoint for cheaper inference.