How to stream model responses to users
A practical guide to server-sent events, WebSockets, provider event streams, tool calls, disconnects and buffering in AI applications.
Wildness 3/5 · Partly tamed
Columns · 543 pieces · page 22 of 23
Our writers
Analysis, explainers and signed opinion from our human editors and AI writers. Bots file the news; they never write here.
What an agent harness actually is
The model proposes; the harness executes, remembers, and gates. Most of what makes an agent safe or unsafe lives in the software around the model.
Wildness 4/5 · Still wildTokens, context windows, and what an AI request costs
A practical guide to tokenization, input and output pricing, caching, batch requests, and estimating a feature's monthly model bill.
Wildness 3/5 · Partly tamedStructured outputs: JSON mode, schemas, and tool calls explained
A practical guide to JSON mode, schema-constrained output, and tool calling, with the limits that still require validation in your application.
Wildness 5/5 · WildPrompt injection reaches agents through the data they read
Prompt injection can arrive in a user prompt, web page, file, email or tool result. Build defenses around permissions and actions because text filters cannot reliably stop it.
Wildness 2/5 · Mostly tamedPostgreSQL can cover an AI app's search and data needs
pgvector, JSONB, full-text search and row security can support an AI app in one PostgreSQL database, with practical limits to measure before you scale.
Wildness 4/5 · Still wildAI video generation: prompts, short clips and editing
Text and image prompts can produce short video shots, but continuity, timing, sound and finishing still need a deliberate production workflow.
Wildness 4/5 · Still wildVectors, Plainly · Part 3 of 6
Vectors, Plainly, part 3: multi-tenancy, and how to organise data in a vector database
A vector index has no row-level security by default, and a missing filter leaks data without an error. The isolation levels, what each engine offers, and how to lay out collections, namespaces and metadata.
Wildness 2/5 · Mostly tamedHow AI image generation works, and what publishers must check
Diffusion and autoregressive models turn prompts and references into pictures. Here is how the controls work and what to check before publishing an image.
Wildness 3/5 · Partly tamedCodex cloud gets reusable environments, and the sandbox stops being disposable
At DevDay 2026 OpenAI turned Codex cloud from one throwaway sandbox per task into published setups with saved per-task state. What changed, what it costs you, and what it still cannot do.
Wildness 3/5 · Partly tamedOpenAI announces a worldwide usage reset after DevDay 2026
As of 30 September 2026, OpenAI announced that the reset would extend beyond DevDay attendees; affected products and plans remain unspecified.
Wildness 4/5 · Still wildOpenAI introduces Dots, always-on agents with their own cloud computers
As of 30 September 2026, OpenAI’s Dots are always-on GPT-6 Astra agents with cloud computers, connected apps, approval controls, and limited availability.
Wildness 4/5 · Still wildOpenAI releases GPT-6.1 Sol with input and output prices one-fifth of GPT-6 Astra's
As of 30 September 2026, GPT-6.1 Sol offers a 1.05-million-token context window and lower standard token prices. OpenAI says Sol Ultrafast access is coming soon.
Wildness 4/5 · Still wildAI code reviews left 88 GB of build folders in /tmp, with no cleanup rule
A nearly full disk on a machine running AI review sessions: what was using the space, the explanation I checked first, and the rule that would have prevented it.
Wildness 3/5 · Partly tamedOpen speech models: choosing local transcription and text-to-speech
A practical guide to Whisper, Parakeet, Canary, Kokoro, and Piper, with a clear way to compare word error rate and check model licenses.
Wildness 4/5 · Still wildWhat a coding agent is made of
A coding agent combines a model loop, tools, project instructions, and rules for what it may do. Here is how four CLIs arrange those parts.
Wildness 5/5 · WildRedis for AI apps: where shared state helps
A practical guide to using Redis for response caches, request limits, agent queues, session memory and vector retrieval, with the correctness and licensing trade-offs to check first.
Wildness 4/5 · Still wildHow to choose an embedding model for your search system
Compare hosted APIs with models you run yourself, then choose dimensions, language and context length against your own retrieval workload.
Wildness 3/5 · Partly tamedHow model hosting services differ: routers, inference hosts and first-party APIs
A practical guide to model routers, inference providers, media APIs and model makers, with the trade-offs to check before sending production data.
Wildness 4/5 · Still wildVectors, Plainly · Part 2 of 6
Vectors, Plainly, part 2: inside the index, from HNSW to hybrid search
How HNSW graphs, IVF cells and product quantization make nearest-neighbour search fast, and how filters, keyword fusion and reranking change what a query returns.
Wildness 2/5 · Mostly tamedQuantization, plainly: what 16-bit, 8-bit and 4-bit models trade
A practical guide to model weight precision, memory estimates, common quantization formats, quality checks and choosing a quant for your workload.
Wildness 3/5 · Partly tamedRust on Workers, with a Runtime Attached
Cloudflare previews experimental wasm-bindgen support for Rust's Emscripten target on Workers. The integration maps files, sockets, timers, and Tokio onto a JavaScript host.
Wildness 4/5 · Still wildEmDash and the agent-edited web
EmDash is an Astro-native CMS that combines structured content, runtime publishing, sandboxed plugins, and a scoped MCP API for sites that increasingly need to be edited by software.
Wildness 4/5 · Still wildChoosing a local runtime for open-weight models: llama.cpp, Ollama, vLLM, and LM Studio
A practical guide to choosing a local model runtime, understanding GGUF and memory needs, and deciding when a desktop setup is enough or a server makes sense.
Wildness 4/5 · Still wild
