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[Other] Show HN: NeuroFlow 55.8x video inference speedup for Vision Transformers PyTorch

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[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! (:

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[Other] Show HN: skills-for-humanity – 171 structured reasoning skills for Claude Code

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

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

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[DevOps] Using Tailscale with an OrbStack VM on macOS Here&#x27;s an example of how to build an Ubuntu VM using OrbStack on macOS and then connect to the VM through Tailscale SSH using an auth key stored in Apple Keychain.<p>For example, I can create a VM on my Mac mini at home that hosts a git repo or even a Forgejo server. A colleague in my Tailnet can then connect to this VM to clone, push or pull source code changes from a coffeeshop or airliner while not exposing the rest of my Mac mini.

[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] Show HN: TryPost – open-source Social Media Scheduler

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[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] Why Ctrl+V won't paste images in Claude Code on WSL, with a fix