ModelsQwen 3.6 Max: How to Plan Around a Preview Model
Max is still a preview tier, which means its specs, weights and price can all move. A procedure for evaluating and depending on a model that has not settled.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Models
ModelsQwen 3.6 vs MiniMax M3: Different Benchmarks, Really
One reports 77.2 on SWE-bench Verified, the other 59.0 on SWE-bench Pro. Those numbers cannot be subtracted, and the real difference is elsewhere.
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ModelsQwen 3.6 vs Qwen 3.5 Coder: General or Specialised
One is a newer general model, the other a code-specialised predecessor. Whether a specialist still beats a stronger generalist at its own job.
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Cost & PricingRate Limit Tiers Explained: RPM, TPM and What Binds First
Provider rate limits are two separate meters and the token one usually binds first. How tiers are assigned, how bursts work, and how to engineer around both.
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AI AgentsRate Limiting Agent Fleets Without Killing Throughput
One API key, many agents, one shared quota. How to build a fleet-wide limiter that respects token budgets, protects interactive work and survives bursts.
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GuidesRedacting Secrets From Prompts Before They Leave Your Machine
Credentials reach model context through error output, config files and git history. Pre-send scanning, why gitignore is no defence, and rotating on suspicion.
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ModelsRegression Testing a Model Upgrade Before It Bites
A new model version is a dependency bump with no changelog. How to gate an upgrade, what to diff, and how to roll back when behaviour shifts.
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FundamentalsRepetition Penalties: Why They Break Code Generation
Frequency and presence penalties suppress loops by punishing tokens the model already used. On code that punishes syntax, and the loop was rarely the real bug.
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AI AgentsReplaying Agent Traces: Debugging a Run You Cannot Reproduce
Agent runs rarely reproduce, so the stored trace is the only evidence. What to capture, how to replay it, and where replay stops being faithful.
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FundamentalsResidual Connections: Why Deep Models Train at All
The one-line change that made hundred-layer networks possible, why the residual stream behaves like a shared bus, and what it explains about pruning.
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FundamentalsRLHF Explained: Training on Comparisons Instead of Answers
Reinforcement learning from human feedback shapes the qualities nobody can write down. The three-stage pipeline, and the failure modes you see as a user.
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AI AgentsRollback Strategies for Agents That Changed Things
An agent stopped halfway through eleven changes. Checkpoints, compensating actions, staging areas and the undo plan you should require before it acts.
ReadShowing 217–228 of 404 articles