Dhanush Kandhan

@dhanu.letretro.com

building for the internet. talking about AI & SaaS. building @letretro.com

hallx v1.1.0 is now available. this release adds claim-level grounding, faithfulness checks, tool-call validation, async support, and Python 3.9 support. a small step toward making llm outputs easier to evaluate and trust. pip install hallx github.com/dhanushk-offl/hallx/releases/tag/v1.1.0

Release v1.1.0 · dhanushk-offl/hallx

What's Changed feat: claim-level grounding, LLM-as-judge, and tool-call validation by @dhanushk-offl in #3 release: bump version to 1.1.0 by @dhanushk-offl in #4 Full Changelog: v1.0.4...v1.1.0

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Introducing letRetro Canvas. Brainstorm ideas, map workflows, plan sprints, and collaborate visually with your team, all on an infinite canvas. Canvas gives your team a flexible workspace to move from discussion to execution. Available now for both Free and Pro teams. Try it → letretro.com/canvas

Canvas — Visual Planning, Ideation & Diagramming | letRetro

Draw, brainstorm, and plan diagrammatically on a real-time canvas. Create use case diagrams for stakeholders and collaborate live with your team. In beta, available for all.

letretro.com

I don't know if it's a coincidence, but every July somehow becomes my learning month. Since 2023, I've spent it picking up new skills, pursuing specializations, or earning certifications. Looks like the streak is still alive.

Fast teams don't just ship. They learn. Sprint retrospectives help startups improve every sprint, not just ship features. We wrote about why they matter and how to run them without unnecessary process. If you're running a startup, this is for you. letretro.com/blog/why-sta...

Why Startup Teams Need Sprint Retrospectives?

Startup retrospectives help lean teams stop repeating mistakes. A practical guide to running sprint retros that work, plus what to look for in a free sprint tool.

letretro.com

curious how you think tanstack query fits into an ai native frontend. As more UI is generated or orchestrated by agents, do you see query caching becoming even more important, or does the abstraction need to evolve? experts thoughts @tkdodo.eu

Retrospectives shouldn't feel like another meeting. They should help teams learn, align, and ship better software. That's what we're building with LetRetro. Give it a try → letretro.com

letRetro's Dashboard

A friend of mine built a @vscode.dev extension that converts Markdown into PDF/ Word documents directly within your editor and codebase. If you work with markdown regularly, give it a try and share your feedback. A star would be appreciated if you find it useful. github.com/anandsundara...

GitHub - anandsundaramoorthysa/markdown-to-pdf-word

Contribute to anandsundaramoorthysa/markdown-to-pdf-word development by creating an account on GitHub.

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Shipped guro v1.1.6. Network monitoring added to the terminal toolkit: → Per-interface bandwidth with history →TCP state breakdown & process-level connections → Protocol stats from /proc/net/snmp → CSV export. Zero new deps. pip install guro --upgrade github.com/dhanushk-off...

Release v1.1.6 — Network Intelligence · dhanushk-offl/guro

What's Changed 🌐 Network Monitoring Dashboard Real-time bandwidth monitoring with per-interface upload/download sparklines, TCP state analysis, process-level connection tracking, and protocol stati...

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Anyone displaying their @medium.com blogs on a personal portfolio? i'm currently using medium's rss feed + rss-to-api to fetch and render posts on my website, but the experience feels a bit glitchy (especially around content rendering and formatting). Curious how others are doing it?

I agree. My DSA has faded because of the gap. I've been spending most of my time building and orchestrating AI agents instead. I'm more focused on making agents code like senior developers. Sometimes they even generate more optimized solutions or find the root cause of a bug that human miss.

unpopular opinion: the hard part of AI agents isnt the model. its the memory architecture. every team is building rag, vector stores, embeddings — but nobody asks: what should the agent actually remember vs forget? thats the unsolved design problem

i was deep into job hunting, drafted 15+ resumes, rewrote the same bullets again and again… so i took it seriously and built resume-parser. it helps AI to curate + structure resumes properly as: • agent skill • CLI • MCP support check it out, break it, star it ↓ github.com/dhanushk-off...

GitHub - dhanushk-offl/resume-parser: An agent skill, CLI tool, npm library, and MCP server that deeply parses resumes using the OpenResume 4-step algorithm

An agent skill, CLI tool, npm library, and MCP server that deeply parses resumes using the OpenResume 4-step algorithm - dhanushk-offl/resume-parser

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