Hey HN, we're Willy and Dan, co-founders of Twill.ai (https://twill.ai/). Twill runs coding CLIs like Claude Code and Codex in isolated cloud sandboxes. You hand it work through Slack, GitHub, Linear, our web app or CLI, and it comes back with a PR, a review, a diagnosis, or a follow-up question. It loops you in when it needs your input, so you stay in control.Demo: https://www.youtube.com/watch?v=oyfTMXVECbsBefore Twill, building with Claude Code locally, we kept hitting three walls1. Parallelization: two tasks that both touch your Docker config or the same infra files are painful to run locally at once, and manual port rebinding and separate build contexts don't scale past a couple of tasks.2. Persistence: close your laptop and the agent stops. We wanted to kick off a batch of tasks before bed and wake up to PRs.3. Trust: giving an autonomous agent full access to your local filesystem and processes is a leap, and a sandbox per task felt safer to run unattended.All three pointed to the same answer: move the agents to the cloud, give each task its own isolated environment.So we built what we wanted. The first version was pure delegation: describe a task, get back a PR. Then multiplayer, so the whole team can talk to the same agent, each in their own thread. Then memory, so "use the existing logger in lib/log.ts, never console.log" becomes a standing instruction on every future task. Then automation: crons for recurring work, event triggers for things like broken CI.This space is crowded. AI labs ship their own coding products (Claude Code, Codex), local IDEs wrap models in your editor, and a wave of startups build custom cloud agents on bespoke harnesses. We take the following path: reuse the lab-native CLIs in cloud sandboxes. Labs will keep pouring RL into their own harnesses, so they only get better over time. That way, no vendor lock-in, and you can pick a different CLI per task or combine them.When you giv
FL score
out of 100
Verdict
high confidence
Competition
12
competitors found, emerging market, funded players
Trend
No signal yet
A cloud platform to delegate coding tasks to lab-native AI agents in sandboxes, aiming to solve parallelization, persistence, and trust issues for developers.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Twill.ai (YC S25) – Delegate to cloud agents, get back PRs”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Twill.ai addresses a real and specific pain for developers struggling with local AI coding agents, leveraging existing LLM CLIs in the cloud for parallelization, persistence, and trust. However, the market is highly competitive with strong, funded incumbents offering similar solutions.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has a clear value proposition in a fast-growing market, but strong competition and significant build complexity for a solo builder pose challenges for profitability and defensibility.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
This project has high problem clarity and good monetization potential for developers, but it's a complex build with an 'anti-niche' focus that might be challenging for a solo builder to execute and differentiate.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Twill.ai has a clear target audience and value proposition for a viable micro-SaaS, with a proven business model, but faces high risk from market saturation and the need to validate its specific differentiation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
This idea addresses a real and growing problem for developers, with strong founder insights, but needs to prove its unique value proposition against many funded competitors.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
An AI pair programmer that provides autocomplete-style suggestions from an AI model trained on billions of lines of code.
Pricing: Individual: $10/month, Business: $19/user/month, Enterprise: $39/user/month.
An AI-first code editor, forked from VS Code, that integrates AI features directly into its core, including multi-file agent capabilities.
Pricing: Credit-based pricing; Enterprise teams needing centralized billing oversight pay $40/user/month.
Anthropic's terminal-based AI coding assistant, designed for deep context understanding and capable of handling complex software tasks.
Pricing: Starting at $20/month.
The world's first fully autonomous AI software engineer that can handle complex engineering tasks end-to-end in its own sandboxed environment.
Pricing: Core plan: starting at $20/month ($2.25 per Agent Compute Unit); Team plan: $500/month; Enterprise plan: Custom pricing.
An AI coding partner that automates entire workflows from issue to PR, understanding codebases, planning changes, writing code, and creating pull requests.
Pricing: Free Trial; Basic: $10/month; Pro: $20/month; Ultra: $60/month. Team plans include $40 in Sweep API credits per seat.
An AI-powered software development platform that accelerates coding workflows through contextual code generation, refactoring, and documentation.
Pricing: Free plan; Basic: $10/month; Pro: $30/month; Enterprise: Custom pricing.
Provides comprehensive AI-powered pull request reviews and advanced insights.
Pricing: Free tier for open-source projects; Pro: $24/month (billed annually) or $30/month (monthly) per developer.
An open-source, terminal-based pair programming tool that works with multiple LLM backends for flexible and controlled code edits.
Pricing: Free; open source (Apache-2.0). Pay only for the model/API if hosted.
Google's AI developer assistant offering conversational workflows and strong reasoning on large codebases, integrated with Google Cloud services.
Pricing: Free for individuals (1,000 requests per day at zero cost with a personal Google account).
OpenAI's terminal-native coding agent, rewritten in Rust for performance, running each task in a network-disabled cloud container for isolated execution.
Pricing: No direct pricing for Codex specifically, but OpenAI models generally have usage-based pricing; alternatives often cite its Pro subscription as expensive ($200/month).
An AI software engineer that operates across the CLI, cloud, and desktop, designed to complete tasks end-to-end without human supervision and open PRs.
Pricing: Pricing not publicly disclosed, but users praise its value for development teams.
An open-source, model-agnostic AI code assistant that provides fast, low-friction coding assistance in VS Code and JetBrains IDEs.
Pricing: Open-source with transparent pricing, pay-per-credit model for AI providers.
What they charge
Recent news
Hacker News, April 10 2026
Product Hunt, April 10 2026
Git AutoReview, April 09 2026
Uvik Software, April 08 2026
Boundev, April 07 2026
Market signals
The AI code assistant market is experiencing significant growth, projected to reach $6.55 billion by 2035 with a CAGR of 5.33% from 2025-2035. Other estimates place the broader AI code tools market (including code generation, review, and testing) at $7–$10 billion in 2025–2026, with a projection to hit $47.3 billion by 2034, growing at a CAGR of 24% from 2025 to 2034. This indicates a rapidly expanding market driven by increasing developer productivity and automated code creation.
What frustrates people
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI