Show HN за 11 мая 2026 г.

I built adamsreview, a Claude Code plugin that runs deeper, multi-stage PR reviews using parallel sub-agents, validation passes, persistent JSON state, and optional ensemble review via Codex CLI and PR bot comments.

On my own PRs, it has been catching dramatically more real bugs than Claude’s built-in /review, /ultrareview, CodeRabbit, Greptile, and Codex’s built-in review, while producing fewer false positives.

adamsreview is six Claude Code slash commands packaged as a plugin: review, codex-review, add, promote, walkthrough, and fix. I modeled it after the built-in /review command and extended it meaningfully.

You can clear context between review stages because state is stored in JSON artifacts on disk, with built-in scripts for keeping it updated.

The walkthrough command uses Claude’s AskUserQuestion feature to walk you through uncertain findings or items needing human review one by one. Then, the fix command dispatches per-fix-group agents and re-reviews the work with Opus, reverting any regressions before committing survivors.

It runs against your regular Claude Code subscription (Max plan recommended), unlike /ultrareview, which charges against your Extra Usage pool.

I would love feedback from Claude Code users, pro devs, and anyone with strong opinions about AI code reviews.

Repo: https://github.com/adamjgmiller/adamsreview

Install: /plugin marketplace add adamjgmiller/adamsreview, /plugin install adamsreview@adamsreview

We were building an agent system and wanted email as a trigger. We decided to take it out and made it a standalone service.

The primary email features we wanted and used for our own agent system:

1. Email threading stays consistent with agent conversation threading 2. Human in the loop review for outbound emails (especially during testing phase) 3. Quick onboarding/offboarding email addresses for agents within minutes 4. Websocket for local agents and at-least-once webhook delivery for Cloud agents

Not yet: DMARC (only SPF/DKIM today), scoped API keys, HA/multi-region (single VM + single Postgres), app-layer email data encryption, compliance attestations (SOC 2/HIPAA).

GitHub: https://github.com/Mnexa-AI/e2a Hosted: https://e2a.dev/

Appreciate any feedback / contributions.

Hello HN!

If you want to connect your agent to a database (say, to build a data analyst chatbot or any kind of agentic app) today you have 2 options: an SQL MCP server or a semantic layer.

SQL MCP is the easiest path to setup, especially if you also have a .md knowledge base which the agent can update. It gets quite messy quickly though, especially if there's many interactions or DB is large. Generated SQL is hard to review if you want to understand where the numbers came from, and related queries can be hard to align and compare.

The natural alternative is a semantic layer, which is an inventory of what data is available/useful (data models) and an interface for querying it using a structured DSL — usually a list of measures, dimensions, filters, with joins etc. handled under the hood.

When we needed a semantic layer at Motley for connecting to our customers' data, we first settled on Cube with custom wiring for multi-tenancy and updating the models on the fly. We quickly hit some limitations which led us to realize existing semantic layers just weren't built for the purpose: they're still a part of the BI world where you want an efficient backend for an essentially static set of human-curated dashboards, whereas agents need to iterate their way to the answer, learning in the process. That's when we built the first version of SLayer, which is now open-source.

Using either SLayer MCP or CLI, agents (and humans) can:

- Explore models, run queries, connect to multiple databases

- Edit columns/measures or create new ones

- Create custom models from SQL or from a query on other models

- Learn from interactions: save and retrieve natural-language memories linked to models, columns or queries, to form a knowledge base

Agents evolve the semantic layer, reuse the results of past interactions, and make fewer mistakes going forward.

A few more features:

- Auto-creation of models from introspecting your DB schema for a warm start

- Embeddability — doesn't need a server running

- Python client for doing data analysis with dataframes

- Schema drift detection and handling

- Expressive DSL with compact, natural representations for arbitrarily deep multistage queries, custom aggregations, time shifts, combining metrics from multiple models, and other features that are tricky to get right in raw SQL

On the roadmap: access controls, caching, and more.

Repo: https://github.com/MotleyAI/slayer

Docs: https://motley-slayer.readthedocs.io/en/latest/

Coding agent management is all the rage right now, and many tools are being created to fill the gap.

As a power user for all tools I've used since I've started my software engineering career, I've always taken the time to test multiple tools thoroughly before deciding on one, and an agentic manager was no different.

I've tested many tools, but ultimately landed on Agent of Empires (AoE for short). Why ? Because it's fast, the development is active and it's feature complete, and easy to contribute to.

So I did (contribute). In my day to day workflow for my job, I need the ability to start agent sessions against multiple repositories, and while AoE had basic support for it, I spent some time extending it to have full project support which has just been merged! (PR #974, more optimizations coming in the next release) With it, each repository get its own worktree and sandbox but the agent can access them all, which makes it ideal for organizations with separate repositories for frontend, backend etc ...

I'm now trying to contribute actively to AoE and we would love more feedback!

Many more features and improvements coming soon too!

https://github.com/njbrake/agent-of-empires

I recently prototyped a web game for a nonprofit to highlight the dangers of phone addiction, but unfortunately I ended up making a really addictive game instead. :-\

I'm sharing this here mainly to serve as an indicator of what can be achieved early-2026 by a senior dev working with Opus 4.7 over 2 days using genuinely collaborative prompting. (ie plan->feedback->iterate)

Hope it provides some inspiration or entertainment - there's a level editor too - maybe Hacker Newsers could share their favourite creations here?

PS: If there's enough demand I'm happy to Open Source this (or DM me) - it's mainly just time restrictions at my end.

Hey HN,

I've been working on a multi-model database called NodeDB.

Originally, i've found out the idea of SurrealDB quite good. However, it doesn't have some graph and vector features that I need. And since it is just a KV wrapper, instead of purpose-built engine, the performance will never be close to the specialized databases (like Neo4j, Pinecone, Clickhouse, etc).

And i've asked myself, what if, there is a database that have the same idea, but built differently? Instead of just treating it as KV database, we build specialized engines for the data.

Besides that, I want it to be able to support my IOT/edge project, where i need offline sync capabilities (Currentyl still in progress).

Will it work?

I put it into test. I've been experimenting and researching for a year, creating multiple versions, and then I created NodeDB.

Disclaimer: It is still in public beta (as of May 2026), but it really excites me if I can make this db work. And I use AI as assistant for coding and planning. It is nearly impossible to do as a solo developer without any AI assistance.

Would love feedback from HN:

- Are there specific features or improvements that would make it more useful?

If you're interested in experimenting or contributing, the repo is here: GitHub Repo: https://github.com/nodedb-lab/nodedb

Looking forward to your thoughts!

R-CPD (Retrograde Cricopharyngeus Dysfunction) is a condition where a muscle in the throat never learned to relax properly, making it impossible to burp. It affects more people than you'd think and causes significant discomfort, extreme bloating, and social anxiety. The most common medical treatment is a botox injection, but it's expensive and not accessible to everyone.

I'm a Software Engineer from Germany and suffered from R-CPD my entire life before curing myself last year. I wanted to make the self-teaching process easier for everyone who comes after me, so I built Learn2Burp. It walks you through exercises with video guidance, builds a workout plan around your specific situation, and includes a burp tracker. There's also a wiki covering the questions I wish I'd had answers to when I started.

If you or someone you know has R-CPD, there's also a dedicated r/noburp community worth checking out.

Add the bot to private or group chat: https://t.me/ChessvisionAiBot

Join Telegram channel: https://t.me/chessvision_ai

Bot's page: https://telegram.chessvision.ai

What it does:

- Recognizes chess positions from images

- Provides engine evaluation, hints, and links to external analysis

- Finds games the positions come from and videos explaining the positions

- You can save positions directly to your Library and study them