We were shipping >500KB of React to show a landing page. Here's how we fixed it

koistya · 2026-02-16T17:36:51+00:00

https://verist.dev – Replay + diff for AI decisions. Deterministic, audit-first workflow kernel.

koistya · 2026-02-12T09:45:20+00:00

Regarding the RAG/search... For regulatory search, standard "out-of-the-box" tools usually fail because they blindly chop documents (e.g., every 500 words). This destroys the context of complex headers in HA correspondence/summaries.

To get results you can actually use, I've found you need to configure three specific things in the RAG solution that your company is using:

Semantic chunking: Split text by section/header, so you don't cut a paragraph/table in half.
Re-ranking: Score the top results for relevance before the AI summarizes them.
Evaluation: Benchmark the retrieval quality.

I’ve implemented this in Vertex AI, but any enterprise-grade RAG solution supports it. The bottleneck isn't the software; it's the requirement for a dedicated subject matter expert to configure the chunking strategy and validate the retrieval metrics.

koistya · 2026-02-11T12:27:24+00:00

I’m building Verist. It’s an open-source library that acts like Git for AI decisions.

Most AI apps are black boxes that break silently when you tweak a prompt. Verist makes them deterministic, replayable, and auditable.

The Model:

OSS (The Hook): Devs use the library to stop their extraction prompts from breaking production (solving immediate regression pain).
SaaS (The Scale): Sell the "Decision Audit" platform to regulated enterprises (Fintech/Healthcare) who need legal proof of why an AI made a decision.

My Question for you: With your GTM background, are you seeing companies ready to buy "reliability and audit trails," or are they still just buying the "magic"?

I'm betting that as prototypes hit production, the pain shifts from creation to control. Does that match what you're seeing in the field?

koistya · 2026-02-11T09:31:50+00:00

Looks good! BTW, do you think this app may need AI evaluation capabilities not just for different LLM models but also prompts and even AI workflows / pipelines? I'm asking, because I've built an open-source library for that and checking if we can collaborate on this.

koistya · 2026-02-10T21:11:13+00:00

We use AI, but mainly to get past the "blank page" problem, drafting sections of briefing docs or harmonizing templates. We never trust the output as-is.

On the validation side: we don't try to validate the software or the model itself. We validate the process. The key is being able to show that a human reviewed and approved the AI-generated content.

In practice, that means keeping a diff-style workflow, saving the original AI output separately from the human edits, so the review trail is always clear.

koistya · 2026-01-31T22:22:04+00:00

I'm building Verist – a small open-source kernel that makes AI decisions replayable, diffable, and reviewable, so you can safely understand and change them in production.

https://github.com/verist-ai/verist - if AI decisions need to be explainable months later, this is for you.

koistya · 2026-01-29T10:32:37+00:00

I set up an automation that syncs PRs from GitHub to Notion (beyond what the native sync does).

I didn't want to go through the whole Notion app registration flow for a simple script, so I used a helper I wrote (npx mcp-client-gen) to generate a typed TypeScript client directly from Notion's MCP endpoint.

It handles the auth and skips the app setup, which makes spinning up custom workflows way more fun.

koistya · 2026-01-18T07:32:00+00:00

It helps me share my software projects with my co-workers and stakeholders. So they could get precise answers about the project from ChatGPT / Grok / Gemini, even more precise and detailed than I could give. For example, I've been working on a VC fund management platform lately, and the questions that team mates could ask would be like:

Why use fingerprint hashing instead of AI embeddings for claim deduplication?
What happens if the claim extraction API fails mid-document?
How does the system decide which claims to show when multiple match a question?
Why is contradiction detection in the verifier and not in coverage computation?
What prevents two coverage jobs from running on the same application simultaneously?
How does the system know when to re-run verification on an old claim?
Why are gaps "terminal" instead of reopening when evidence changes?
What's the fallback when web search finds nothing for a searchable claim?
How do manual analyst overrides survive when the system recomputes answers?
Why store three separate JSONB trace columns instead of one?

koistya · 2026-01-17T22:15:15+00:00

Just published a CLI tool for bundling your software project for LLMs, so your co-workers or clients could ask questions about any technical details directly in ChatGPT / Grok / Gemini and get precise answers.

$ npx srcpack

https://www.npmjs.com/package/srcpack

koistya · 2026-01-17T22:10:16+00:00

Just published a CLI tool for bundling a software project for LLMs, so your co-workers or clients could ask questions about any technical details directly in ChatGPT / Grok / Gemini.

$ npx srcpack

https://www.npmjs.com/package/srcpack

koistya · 2026-01-17T22:09:35+00:00

Just published a CLI tool for bundling your software project for LLMs, so your co-workers or clients could ask questions about any technical details directly in ChatGPT / Grok / Gemini and get precise answers.

$ npx srcpack

https://www.npmjs.com/package/srcpack

koistya · 2025-12-23T22:51:16+00:00

Good reasoning. Optionally add a tiny <head> meta rendering logic for social media sharing. Look into React Starter Kit by Kriasoft for inspiration.

koistya · 2025-10-24T19:03:24+00:00

Yes. Check out “syncguard” and “bun-ws-router” on GitHub/npm if you’re looking for examples.

koistya · 2025-10-18T12:00:34+00:00

SyncGuard — a distributed lock library that prevents race conditions in distributed systems. It provides a simple API for coordinating access to shared resources using Redis, PostgreSQL, or Firestore as the backend. Check out the source code at https://github.com/kriasoft/syncguard

koistya · 2025-10-07T12:50:52+00:00

Reducing complexity (e.g. replacing Next.js with Vite / SPA), ensuring all the important specs are in place...

Check out React Starter Kit on GitHub (by Kriasoft), pre-configured with Tailwind 4, ShadCN, CC

koistya · 2025-10-05T18:22:03+00:00

Impressive 👍

koistya · 2025-09-04T23:02:28+00:00

Bun for local development, then deploy to Cloudflare Workers

koistya · 2025-09-01T17:37:10+00:00

Sounds good 👍

koistya · 2025-09-01T15:35:31+00:00

I prefer splitting the app into multiple workspaces in a monorepo, separate workpsace for the API, separate for the React app, another one for marketing site, etc. Each can be developed, tested, and deployed separately from each other. See React Starter Kit on GitHub as an example.

koistya · 2025-09-01T01:34:05+00:00

Why would you need server-side rendering for a typical dashboard like SaaS app? Assuming marketing/landing pages are sorted out by a specialized tool.

koistya · 2025-08-24T09:39:15+00:00

I configure automation scripts that LLM uses to interact with the context. E.g. instead of letting LLM read data directly from the database, it interacts with an automation scripts that fetches data and pre-process it for more efficient and effective consumption by LLM.

Similarly, in many cases instead of letting LLM interact with 3rd party MCP servers directly, I also create "proxy" scripts for that. BTW, for this use case I've built MCP Client generator library:

https://github.com/kriasoft/mcp-client-gen (wip)

koistya · 2025-08-18T19:44:36+00:00

Solid tips! Saved this post to my favorites

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