🛠️ All DevTools

[DevOps] Launch HN: Minicor (YC P26) – Windows desktop automations at scale Hey we’re Faiz and Saheed and we built Minicor so AI companies who need to integrate to desktop systems with no API can quickly build scalable desktop RPAs. Demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MD0GHZIJ1cw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=MD0GHZIJ1cw</a><p>We were working on non-RPA integrations when a customer promised to sign a deal in 2 days if we could unblock a sale of theirs that involved integrating with a clinic’s Windows based medical record system. We didn’t know it at the time but it turns out that building desktop RPAs at scale is extremely difficult because scripting is hard (learning the system, defining the automation, UIs changing constantly), orchestration is hard (is the VM up? queuing, parallelizing) and debugging is hard (zero observability, false positives, cascading failures). 30%+ failure rates are not uncommon. At scale we’ve seen cases of failed RPAs leading to thousands of support tickets a month.<p>To solve the problems we were facing, we built an MCP that Claude Code&#x2F;Codex can use to navigate a virtual machine running desktop software with Python to create RPA workflows. The RPA workflows run as Python scripts for speed, cost, and determinism. These workflows can be triggered by API following any input&#x2F;output schema specified, with video replays and logs stored with each run. The MCP can debug RPAs and make changes to the underlying code, all of which are version controlled. We also built tools for cloning VMs for parallelizing RPAs, and handling 2FA&#x2F;OTP challenges. Plus since workflows are code based: we were also able to add triggers for Slack notifications, human-in-the-loop steps, or call an LLM to verify the state of a VM by passing a screenshot.<p>Would love to hear your feedback and if you have any RPA horror stories! (:

[Other] GitHub Actions down again today

[Other] Incident with Actions and Pages

[Other] Logseq Doctor: Heal your flat old Markdown files before importing them to Logseq

[Other] Show HN: Pgcraft – a lazygit-style TUI for Postgres

[Other] Using AI to write better code more slowly

[Other] Show HN: OpenBrief – Local-first video downloader/summarizer OpenBrief is basically a GUI for yt-dlp with some AI on top — paste a link, it downloads locally, and transcription and voice generation run with local AI on your machine. Summaries and chat over the transcript use an LLM, which is bring-your-own-key for now. It&#x27;s open source and free.

[Other] Riscrithm – An intuitive RISC-V assembler and optimizer coded in Go

[Other] Introducing USB4STREAM Protocol for Linux – Opening Up Some Nifty Uses for USB4

[API/SDK] Launch HN: Chert (YC P26) – Twilio for iMessage Hey HN! We’re Gary and Ian, and we’re building Chert (<a href="https:&#x2F;&#x2F;www.trychert.com&#x2F;">https:&#x2F;&#x2F;www.trychert.com&#x2F;</a>), an API for businesses to send, receive, and automate iMessage conversations at scale. Check out our demo: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=SRdwvVxMMoI" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=SRdwvVxMMoI</a>.<p>We originally started by building products on top of iMessage because the blue bubble interface, typing indicators, and reactions made agentic conversations feel more human than ones on SMS&#x2F;RCS. These included a one-shot iMessage agent builder that reached 2,000 users in one week and an automated iMessage outbound sequencer that sent thousands of outbound messages per day.<p>The hard part is that iMessage does not have a native API like SMS&#x2F;RCS. Sending and receiving iMessages requires a separate infrastructure that is difficult to set up and maintain, especially at scale.<p>As we talked to more companies, we realized that the highest-volume use cases for iMessage were not B2C agents or even sales. They were things like customer service, missed-call text-back, cart abandonment, and inbound lead capture in verticals like home services, DTC brands, and property management that drive the highest volume.<p>Furthermore, these companies often need additional support, such as custom infrastructure setup (e.g. contact card, area code, or local worker sessions), integration support with their existing SMS&#x2F;RCS or voice agent systems, and a reliable way to scale their volume over time.<p>We built Chert to be an infrastructure layer for businesses to handle iMessage conversations at scale. Businesses can use our API to send and receive iMessages programmatically, route replies to humans or agents, and integrate conversations into the systems they already use.<p>To maintain stability across both outbound and inbound use cases, we built phone line health checks and SMS&#x2F;RCS fallback systems. We also integrate with existing SMS&#x2F;RCS systems, voice agents, CRMs such as Salesforce, HubSpot, and Attio, and tools like Slack. Finally, we let businesses reliably scale from a few test lines to hundreds of lines with automated line provisioning and a usage-based pricing structure.<p>We’re working with companies doing conversational messaging in DTC, sports programs, property management, and home services at the scale of hundreds of lines.<p>We’d love to hear your thoughts on this and other similar verticals where iMessage could be useful. All comments welcome!

[Other] C extensions, portability, and alternative compilers

[Other] Show HN: Volt – front end tooling for Phoenix that runs inside the BEAM

[Other] The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

[Other] Show HN: Geomatic – a command-driven geometry studio enabled with autodiff All commands have the format `output = \func inputs` or just `\function inputs`. Points and scalars are built on the fly. Eg `\line a b` to an empty canvas creates points `a` and `b`, and joins them with a line.<p>One can use broadcasting semantics similar to NumPy and PyTorch in a visual setting (imagine creating a list of circles where one dim corresponds to radius and another to the center). One can also use backpropagation, run gradient descent or visualize vector fields. Almost everything is reactive so changing a variable updates all of the downstream geometry. It also allows anyone to write and load their own visualization, which can be broadcasted and differentiated through.

[Other] Defeating Git Rigour Fatigue with Jujutsu

[Other] DeepSeek reasonix, DeepSeek native coding agent with high caching and low cost Related ongoing thread:<p><i>DeepSeek makes the V4 Pro price discount permanent</i> - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48237663">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=48237663</a> - May 2026 (384 comments)

[Other] Constraint Decay: The Fragility of LLM Agents in Back End Code Generation

[Other] AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods

[CLI Tool] Show HN: Kanban CLI (A local-first, agent-first task manager for the terminal) Hello HN,<p>Ever since agents have become increasingly common in development, I&#x27;ve been scratching my head as to how to control their randomness. Recently, I decided to emulate an issue-tracking and project-management tool for agent-driven workflows.<p>Kanban is a Rust-based coordination layer designed to provide a feature-rich terminal interface and enforce rigorous workflows. It aims to be versatile and extendable, made to be tailored to any preferred flow. It comes with full git integration and guardrails such that only what truly benefits a project can go through.<p>The workflow boils down to 4 steps:<p>1. The model reads the skill to contextualize the requirements<p>2. It authenticates and receives a strict, schema-validated JSON payload outlining exact files, context, and acceptance criteria<p>3. Implementation is performed within an automatically isolated Git worktree and branch. The tool tracks progress (e.g., verifying all files were edited) before the task is submitted for review<p>4. A reviewer (preferably a human) evaluates the submission and manually transitions the task to &quot;Done,&quot; which triggers the final merge and cleans up the task-specific environment.<p>The tool significantly decreases the agent development time, while increasing the human planning phase.<p>There is more to it than I can cover here, so I&#x27;d be happy to answer any questions about the architecture, the workflow, or the insights I gained while using it. For more information, I recommend skimming the README, which acts as an index to all documentation files.<p>Repo: <a href="https:&#x2F;&#x2F;codeberg.org&#x2F;hydrafog&#x2F;kanban" rel="nofollow">https:&#x2F;&#x2F;codeberg.org&#x2F;hydrafog&#x2F;kanban</a>

[Other] Mastering Dyalog APL