How I code from anywhere at any time
TL;DR: I work on many repos at once, and on the same repo in different workspaces, parallelizing everything with agents. Terminal apps felt too constrained...
We're in the jQuery era of AI. Who'll be the React of agents?
TL;DR: Current agent harnesses are what jQuery was in 2006: pragmatic abstractions over an unstable primitive. The "React of agents" doesn't exist yet, and...
Memory is the bottleneck for self-driving agents
TL;DR: Self-driving agents are the destination everyone agrees on. The bottleneck isn't autonomy or planning - it's memory. Forkable experience is the...
Why 10 million tokens is the only memory benchmark that matters
TL;DR: Memory benchmarks died when context windows hit 1M tokens — just dump everything in the prompt. BEAM tests at 10M where that trick fails. Hindsight...
That's not how you do business
TL;DR - Supermemory published a "~99% SOTA" memory benchmark result that was actually a stunt - and the backlash was immediate and deserved. - Gaming...
Open source is a trust system. AI is breaking the contract.
> **TL;DR** > - The OSS contribution model is breaking - not because AI writes bad code, but because it lets people contribute without understanding what...
Human attention defragmentation: flow, fatigue, and AI coding
TL;DR: AI coding tools don't just accelerate output - they fragment attention in ways traditional programming never did. Something shifted a few months into...
AI won't replace engineers, it will replace project managers
TL;DR: AI coding agents collapse the translation layer between users and code - the exact thing PMs exist to provide. Engineers who move into that space are...
Not all agents are the same: task agents vs interaction agents
TL;DR: Task agents and interaction agents have fundamentally different memory requirements. Latency budgets, retrieval quality bars, and even the question of...
It was never about the code
In May 2025, I spent two weeks building the UI for a project we were launching at Vectorize. The product had a dataplane, backend services, the whole stack-...
RLM is half a paradigm
TL;DR: RLM excels at within-session context management through prompt-as-variable decomposition. But it's blind to cross-session knowledge - no persistence,...
Not all context is equal: hierarchical memory for AI agents
TL;DR: Not all context is equal. A curated company policy shouldn't have the same priority as a random Slack message from three months ago. Hindsight's...
Cache the reasoning, not the answer
TL;DR: Agents pay a synthesis tax-reasoning to the same answers hundreds of times. Semantic caching can't handle staleness or evidence chains. Mental models...
File-based agent memory: great demo, good luck in prod
TL;DR: The "file is all you need" movement benchmarks well because benchmarks are small. In production, you'll hit context rot, multi-hop limitations, and...
From facts to insights: how observations work in Hindsight
TL;DR: Observations consolidate multiple facts into synthesized patterns via background LLM processing. They track evidence chains to supporting facts, run...
What learning actually means for AI agents
TL;DR: Storing and retrieving raw facts works for simple Q&A, but agents that run over time need more. They need to learn from experience, adapt when things...