Updates and recent posts about GPT-5.3-Codex..

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@mikeschinkel started using tool Go , 5 days, 6 hours ago.

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@mikeschinkel started using tool GNU/Linux , 5 days, 6 hours ago.

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@mikeschinkel started using tool Docker , 5 days, 6 hours ago.

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@varbear shared a link, 6 days, 22 hours ago

FAUN.dev()

I built a programming language using Claude Code

Cutlet usesClaude Code. The LLM emits every line. Source, build steps, and examples live on GitHub. It runs on macOS and Linux and ships aREPL. It supports arrays, strings, double numbers, a vectorizingmeta-operator, zip/filter indexing, prototypal inheritance, and a mark-and-sweepGC. Development ra.. read more

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@varbear shared a link, 6 days, 22 hours ago

FAUN.dev()

Using Rust and Postgres for everything: patterns learned over the years

Rust and PostgreSQL are considered the best tools in the software world due to their performance and reliability. Rewriting a backend service from Go to Rust led to significant improvements in processing speed and memory usage. Using sqlx for database operations and leveraging PostgreSQL features li.. read more

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@varbear shared a link, 6 days, 22 hours ago

FAUN.dev()

Why value streams and capability maps are your new governance control plane

The piece flips enterprise AI fromgenerativetoagentic. Agents getstructured autonomyto perceive, plan, and execute across systems. It turnsvalue streammaps into a control plane withautonomy zones,halt-on-exceptiongates, cryptographicflight recorders, andpolicy-as-code. Result: less hallucination and.. read more

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@varbear shared a link, 6 days, 22 hours ago

FAUN.dev()

A new chapter for the Nix language, courtesy of WebAssembly

Determinate Nix introduces experimental WebAssembly host calls. It lets Nix invoke Wasm modules, pass and return complex Nix values, and support Rust, C++, and Zig toolchains. It runs on Wasmtime/Cranelift and slashes runtime and memory: Fibonacci test 0.33s vs 79.33s, 30MB vs 4.5GB. Per-call instan.. read more

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@varbear shared a link, 6 days, 22 hours ago

FAUN.dev()

Cracking the Python Monorepo

Outlines a Python monorepo setup that pairsuvworkspaces withDaggerandBuildKitcaching. Builds container stages programmatically. Keeps things cache-friendly and predictable. Parsespyproject.tomland extracts the workspace graph. Copies required local packages into intermediate stages. Installs them in.. read more

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@kaptain shared a link, 6 days, 22 hours ago

FAUN.dev()

Running Agents on Kubernetes with Agent Sandbox

Agent Sandbox unveils the Sandbox CRD to map long-lived, singleton AI agents onto Kubernetes. It adds stable identity and lifecycle primitives. It supports runtimes like gVisor and Kata Containers. It enables zero-scale resume. It includes SandboxWarmPool with SandboxClaim and SandboxTemplate to kil.. read more

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@kaptain shared a link, 6 days, 22 hours ago

FAUN.dev()

RAM is getting expensive, so squeeze the most from it

The Register contrastszramandzswap. It flags a patch that claims up to 50% fasterzramops. It notes Fedora enableszramby default. It details thatzramprovides compressed in‑RAM swap (LZ4).zswapcompresses pages before writing to disk and requires on‑disk swap... read more

GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.