Baton – A desktop app for developing with AI agents
Hi,I built this because running multiple Claude Code agents across multiple IDE and terminal windows was getting messy. Like many, I went from working at one thing at the time, to multiple, and it was all changing quite fast.I needed one place to see all my agents and worktrees, seamlessly switch between them, monitor their status and once their done, review their changes. I also wanted to quickly spin up new agents in isolated worktrees whenever an idea came to mind.I've been building Baton from within Baton for a while now, which has been a pretty fun loop. Would love to hear what you think!
FL score
out of 100
Verdict
high confidence
Competition
10
competitors found, emerging market, funded players
Trend
No signal yet
A desktop app for orchestrating and managing multiple AI agents and their worktrees for developers.
The pain
The gap
Build angle
Strengths
- Addresses a real, daily pain for a growing segment of developers adopting multi-agent workflows.
- Clear value proposition: streamlines complex workflows, reduces context switching, improves efficiency.
- Good monetization potential with proven subscription models in the developer tool space.
- The market for AI agents is rapidly expanding, indicating future growth and urgency for orchestration solutions.
Risks
- High build complexity for a solo builder to create a robust desktop app with deep, reliable integrations for diverse and evolving AI agents.
- Achieving differentiation in a crowded market with well-funded incumbents offering general AI coding tools.
- Potential for feature bloat or integration fragility as new agents and LLMs emerge.
- Developer inertia against adopting yet another tool, requiring significant value to overcome switching costs.
Next steps
- Conduct targeted interviews with developers heavily using multiple AI agents to confirm specific pain points and validate the most critical features they'd pay for.
- Prioritize initial agent integrations based on user demand, focusing on a minimal viable solution that solves the core 'messy workflow' problem for a specific developer persona.
- Develop a clear marketing strategy to highlight the unique orchestration capabilities over general AI IDEs or low-level frameworks.
- Consider open-sourcing parts of the integration layer to leverage community contributions and mitigate solo builder complexity.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 25
- Solution gap
- 18
- Willingness to pay
- 20
- Buildability
- 4
The idea addresses a real and growing pain for developers managing multiple AI agents, with a clear gap for a dedicated orchestration desktop app. However, the complexity of building a truly comprehensive solution for disparate agents is high for a solo builder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market demand and value proposition, but differentiation needs to be sharp and build complexity is non-trivial for a solo founder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem and good monetization, but high complexity and creator fit risk for a solo operation.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong target audience and value, but distribution and integration risks are notable.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a real, specific developer pain with good future potential, but needs validation on the narrowest paid wedge and broad observation.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct targeted interviews with developers heavily using multiple AI agents to confirm specific pain points and validate the most critical features they'd pay for.
- Prioritize initial agent integrations based on user demand, focusing on a minimal viable solution that solves the core 'messy workflow' problem for a specific developer persona.
- Develop a clear marketing strategy to highlight the unique orchestration capabilities over general AI IDEs or low-level frameworks.
- Consider open-sourcing parts of the integration layer to leverage community contributions and mitigate solo builder complexity.
Who is already there, emerging market
A desktop AI coding IDE that integrates AI directly into the editor for codebase-aware editing, refactoring, and debugging.
Pricing: Free, Pro $20/month, Business $200/month.
An AI coding assistant known for deep-context, terminal-first work across large repositories and test-driven debugging.
Pricing: Free, Pro $20/month, Max $200/month.
An AI coding assistant that provides real-time code suggestions, contextual chat, and repository-aware assistance directly within the editor.
Pricing: $10/month individual, $19/month teams, free for students and open-source contributors.
- AutoGPTVisit
An open-source AI platform that allows users to automate multistep projects and complex workflows with AI agents.
Pricing: Open-source, therefore free to use, but may incur costs for API usage with LLMs.
An autonomous AI agent platform designed to execute complex tasks using a goal-based approach, acting as a 'Digital Employee'.
Pricing: Free plan with 100 messages/month, Pro $15/month, Ultimate $59/month, custom Enterprise plans.
An open-source framework for developing applications that integrate large language models with external tools, knowledge bases, and APIs.
Pricing: Open-source (free framework), but offers a platform called LangSmith with pricing not explicitly detailed for agent development, though it has various products for observability, evaluation, and deployment.
- SuperAGIVisit
A dev-first open-source autonomous AI agent framework for building, managing, and running useful autonomous agents.
Pricing: Open-source (free framework), with potential costs for underlying LLM API usage.
An AI platform by Fetch.ai that streamlines the research, development, and administration of AI agents, improving productivity and scalability.
Pricing: Free tier available for basic features, subscription plans tailored to usage scales and needs. The open-source framework is free.
- Invoke StudioVisit
A desktop AI coding IDE with visual planning boards, a design canvas, and intelligent agents to map features and design pages.
Pricing: Free with your own API keys for various LLMs.
- SmythOSVisit
An AI agent development and deployment platform that combines a visual builder with no-code options for creating and managing AI agents.
Pricing: Pricing not publicly disclosed, likely custom or enterprise-focused.
What they charge
Recent news
- Invoke: Agentic coding IDE with visual planning boards and canvas
Product Hunt, March 30 2026
- The best AI coding agents in 2026
Product Hunt, April 01 2026
- The best AI code editors in 2025
Product Hunt, March 31 2026
- AI Pricing Guide March 2026
Clutch, March 27 2026
- AI Customer Service Agent Pricing Comparison: The Complete 2026 Guide
Clutch, March 12 2026
Market signals
The market for AI agent development tools is growing rapidly, evolving from basic coding assistants to sophisticated platforms for orchestrating multiple autonomous agents. Recent funding rounds indicate significant investment in the broader AI development space, with a focus on enterprise solutions and tools that simplify complex AI workflows for a wider range of users. The trend is towards more integrated development environments (IDEs) that offer visual planning, multi-agent orchestration, and enhanced collaboration features.
What frustrates people
- SuperAGI is code-centric and requires more technical proficiency, lacking a visual builder or no-code editor.
- Outreach (an alternative to SuperAGI in sales automation) is not ideal for lean GTM teams due to its pricing and setup, and feels like overpaying for features.
- JetBrains AI Assistant has moderate autocomplete speed and limited architectural context across large codebases, requiring JetBrains IDE lock-in.
- Traditional IDEs with AI features, like GitHub Copilot, occasionally struggle with intricate logic.
- ChatGPT, while versatile, may produce subtle code errors.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
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Postgres extension for BM25 relevance-ranked full-text search
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
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Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
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