explore
Pick a thread.
Every story we have published, by topic. Tap a tile to filter — no reloads, just the thread you want to pull.
ai
20 stories
OpenAI Acquires Ona to Give Codex Agents a Persistent Home in Enterprise Clouds
OpenAI is acquiring Ona to bring secure, persistent cloud environments to Codex, letting AI coding agents run long-horizon tasks without losing state or context.
Signal · Jun 19, 2026 · 3 min read
The best AI coding assistants in 2026
For most developers, the best AI coding assistant is GitHub Copilot — because it fits directly into the editors you already use, has the broadest language support, and the model quality is now genuinely competitive. If you live in the terminal or want deeper reasoning on hard problems, read on.
BitByteCore Research · Jun 19, 2026 · 10 min read
Small AI Models Are Quietly Winning in Production
Frontier models get the headlines, but inside real companies, smaller, cheaper, faster models are doing the actual work. Here's why, and what it costs when you ignore them.
Best Work · Jun 14, 2026 · 4 min read
On-device vs cloud AI: what actually leaves your device
The real difference between on-device and cloud AI is not speed or smarts, it is what leaves your device, and who can see it.
Research Desk · Jun 14, 2026 · 1 min read
How to run a local LLM on your laptop
You can run a capable AI model entirely on your own laptop, private, offline, and free to run. Here is the fast path, plus the trade-offs nobody mentions.
Research Desk · Jun 14, 2026 · 2 min read
AI agents in production: the honest 2026 state of play
Every vendor has an agent demo that looks like magic. Strip that away and the real picture is more useful: what AI agents reliably do in production today, where they still break, and what your team should actually ship this quarter.
Best Work · Jun 14, 2026 · 8 min read
Small Models Are Quietly Taking Over the Easy Work
Most production AI tasks are routine, and a new class of small models handles them at a fraction of the cost. The frontier models are becoming the exception, not the default.
Adil R. · Jun 13, 2026 · 3 min read
On-Device AI Is Really a Bet on Privacy and Latency
Running models on the phone instead of the cloud is often pitched as a cost play. The durable reasons are latency and data control, and they change what kinds of features get built.
Muniba K. · Jun 12, 2026 · 3 min read
Open-Weight Models Changed Who Controls the AI Stack
Open-weight models are good enough that many teams no longer depend on a single vendor's API. The shift is less about cost and more about control and lock-in.
BitByteCore Research · Jun 12, 2026 · 3 min read
The Real Cost of AI Is Inference, Not Training
Training a model is a one-time headline number. Inference is the recurring bill that scales with every user and every request, and it is what quietly decides whether an AI product survives.
Adil R. · Jun 11, 2026 · 3 min read
AI Agents Are Moving From Demos to Narrow Jobs
The viral agent demos promised software that does everything. What actually ships are agents scoped to one job with tight guardrails, and that narrowing is the point.
Muniba K. · Jun 10, 2026 · 3 min read
How to choose the right quantization for a local LLM
Decode the Q4, Q5, and Q8 labels on model files, understand what bits-per-weight actually costs you, and pick a quantization that fits your RAM without wrecking quality.
BitByteCore Research · May 24, 2026 · 4 min read
How to build a basic RAG pipeline for a local LLM
Wire up retrieval-augmented generation from scratch: chunk your documents, embed them, store the vectors, and feed the right context into a local model so it answers from your data.
Adil R. · May 23, 2026 · 4 min read
How to fine-tune a small language model with LoRA
Adapt a small open model to your task using LoRA: prepare a clean instruction dataset, train lightweight adapters, and know when fine-tuning is the wrong tool entirely.
Muniba K. · May 22, 2026 · 4 min read
Choosing the right model size for your task
Bigger is not automatically better. A decision framework for matching model size to the job, the latency budget, and the hardware you actually have.
Adil R. · May 18, 2026 · 4 min read
How a transformer model actually works
Attention is not the model reading your text like a person. It is a weighted lookup that lets every word pull context from every other word at once.
Muniba K. · May 13, 2026 · 4 min read
The real difference between training and inference
Training is when a model's weights change. Inference is when they do not. Almost every confused claim about AI 'learning from your chats' lives in that gap.
BitByteCore Research · May 12, 2026 · 4 min read
What a context window actually is
A context window is not memory. It is the fixed amount of text a model can look at in a single pass, and everything outside it simply does not exist to the model.
Adil R. · May 11, 2026 · 4 min read
What RAG actually is and is not
RAG does not teach a model new facts. It fetches relevant text and pastes it into the prompt, so the model answers from documents instead of memory.
Muniba K. · May 10, 2026 · 4 min read
How AI agents work and where they break
An agent is a language model in a loop with tools. The intelligence is real, but the failures compound, and most break because small errors chain into big ones.
BitByteCore Research · May 9, 2026 · 4 min read