Technical deep dives, architecture decisions, and updates from the team.
February 2026 · 8 min read
The AI agent space has a trust problem. Fully autonomous agents make promises they can't keep — they hallucinate, make wrong assumptions, and produce output that requires more time to fix than it would have taken to do the work manually. On the other end, basic chat interfaces require you to manage every step yourself, turning complex projects into tedious back-and-forth conversations.
ALIN's Time-Budgeted Work Orders solve this by introducing a third option: bounded autonomy. A TBWO is a formal contract between you and the AI. You define the objective and constraints; ALIN generates a plan; you approve or modify it; then specialized Agent Pods execute the work with checkpoint gating at defined intervals. The key innovation is that every TBWO produces a receipt — a detailed audit trail documenting every decision made, every assumption taken, every tool called, and every file produced.
This isn't just documentation. Receipts are the foundation of trust. When you can see exactly what the AI did and why, you don't need to blindly trust the output. You can verify it. And when ALIN makes a mistake, the receipt tells you exactly where things went wrong, so you can correct the pattern and ALIN learns not to repeat it.
The time budget is equally important. Most AI agents either try to do everything at once (and fail) or do the minimum (and disappoint). TBWO's time budget directly controls complexity: 10 minutes gets you a quick draft; 45 minutes gets you a comprehensive, polished result with multiple review passes. The planner scales its approach to match the budget — more phases, more tasks, more quality iterations for larger budgets. This makes the output predictable and the cost controllable.
We believe bounded autonomy is the correct level of AI agency for productive work. Not too little (basic chat), not too much (autonomous agents with no oversight), but just right — enough freedom to get real work done, enough accountability to trust the results.
January 2026 · 10 min read
Every AI chat tool claims to have "memory." Most of them mean they prepend your last few messages to the context window. That's not memory — it's a buffer. Real memory requires structure: different types of information need different retention policies, different access patterns, and different consolidation rules.
ALIN's 8-layer memory system is modeled loosely on human cognitive architecture. Layer 0 (Immediate) is your current session — volatile, gone when you close the tab. Layer 1 (Short-Term) persists for 24 hours — yesterday's research results, a code snippet you tried. Layer 2 (Working) is project-scoped, lasting 7 days. These three layers handle the fast-moving, ephemeral context that most AI tools ignore entirely.
Layers 3-4 are permanent knowledge stores. Episodic memory (Layer 3) records timestamped experiences — "On Tuesday we refactored the auth module and the user preferred functional components over classes." Semantic memory (Layer 4) stores facts — "The project uses React 18, TypeScript 5, and Tailwind CSS." These layers consolidate automatically: repeated episodic patterns get promoted to semantic facts.
Layer 5 (Relational) models you as a user — your communication style, skill level, preferences, pet peeves. Layer 6 (Notes) stores things you explicitly tell ALIN to remember. Layer 7 (Self-Model) stores ALIN's understanding of its own capabilities.
The result is an AI that genuinely improves with use. Session 1, ALIN doesn't know your preferences. Session 10, it knows your preferred tech stack, coding style, and communication preferences. Session 50, it has a statistical model of which types of tasks it handles well for you and which ones need more oversight. That's not a gimmick — it's the compound interest of structured memory.
January 2026 · 5 min read
Every AI tool asks you to trust its output. ALIN shows its work.
When a TBWO completes, you get a receipt. Not a summary — a receipt. It lists every decision point the AI encountered, what options it considered, what it chose and why, what tools it called, how long each step took, what files were produced, and what quality assessment the AI gives its own work.
This level of transparency serves three purposes. First, it lets you verify the output without having to trace through every file manually. If the receipt says "chose React over vanilla HTML because of the form complexity requirement," you know the reasoning and can evaluate whether it was sound. Second, it creates an audit trail for team environments where accountability matters. Third — and this is the most important — it feeds into ALIN's self-model. When you mark a receipt decision as "wrong" or override a choice, ALIN learns from that correction. After 3+ corrections in the same direction, the pattern is promoted to permanent memory and ALIN's behavior changes automatically.
Receipts are the mechanism that turns ALIN from a static tool into a learning system. Without receipts, corrections are one-off overrides. With receipts, corrections are training data.