checkpoints
checkpoints at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Build Smarter AI Agents by Adding Human Oversight Where It Matters Most”, “Postgres: Your Durable Orchestrator for Workflows Without External Tools”, and “Porting Doom's renderer into a transformer with zero training”. Autonomous AI agents can read, retrieve, reason, and trigger actions in seconds, useful speed, but real risk when the next step touches money, customer records, or live systems. Most teams assume durable workflows demand an external orchestrator, but Postgres can handle that heavy lifting on its own. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every checkpoints story on Beyond Market Intelligence, newest first.
Build Smarter AI Agents by Adding Human Oversight Where It Matters Most
Autonomous AI agents can read, retrieve, reason, and trigger actions in seconds, useful speed, but real risk when the next step touches money, customer records, or live systems. Human-in-the-loop checkpoints add control exactly where a recommendation is about to become action. That's the smart design. For deeper context, our piece on Anthropic restricting internal AI tests from live internet shows how even the most advanced labs are rethinking control.
Postgres: Your Durable Orchestrator for Workflows Without External Tools
Most teams assume durable workflows demand an external orchestrator, but Postgres can handle that heavy lifting on its own. Raman Varma shows how the database itself becomes the state store and coordination layer, using `SKIP LOCKED` for concurrent processing, primary-key checkpoints for idempotency, and leases to survive crashes. Even workflow sleeps and human approvals persist as database state. It's a pragmatic, accessible approach for teams already comfortable with Postgres.
Porting Doom's renderer into a transformer with zero training
A 21-billion-parameter transformer just rendered Doom's iconic first frame, and it never saw a single training example. The trick: a custom compiler that bakes a computation graph directly into weights, then runs the classic renderer inside that fixed structure. The result is a standard Hugging Face checkpoint, no special code needed. One frame takes 3,614 prompt tokens plus 53,747 generated ones, about 40 minutes on a B200. That is 35 frames per day, a fitting pace for a project this gloriously impractical.