LangAlpha – what if Claude Code was built for Wall Street?
Some technical context on what we ran into building this.MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80 tools across our servers and the prompt cost is the same whether a server has 3 tools or 30. This part isn't finance-specific, it works with any MCP server.The other big thing was making research actually persist across sessions. Most agents treat a single deliverable (a PDF, a spreadsheet) as the end goal. In investing that's day one. You update the model when earnings drop, re-run comps when a competitor reports, keep layering new analysis on old. But try doing that across agent sessions, files don't carry over, you re-paste context every time. So we built everything around workspaces. Each one maps to a persistent sandbox, one per research goal. The agent maintains its own memory file with findings and a file index that gets re-read before every LLM call. Come back a week later, start a new thread, it picks up where it left off.We also wanted the agent to have real domain context the way Claude Code has codebase context. Portfolio, watchlist, risk tolerance, financial data sources, all injected into every call. Existing AI investing platforms have some of that but nothing close to what a proper agent harness can do. We wanted both and couldn't find it, so we built it and open-sourced the whole thing.
FL score
out of 100
Verdict
high confidence
Competition
9
competitors found, emerging market, funded players
Trend
No signal yet
An open-source AI agent framework for Wall Street, solving token limits and persistence for financial data analysis, but faces high build complexity and challenging monetization in a crowded market.
The pain
The gap
Build angle
Strengths
- Addresses a specific, technical, and severe pain for a high-value audience (Wall Street).
- Innovative approach to token management and persistent agent memory.
- Large and rapidly growing market for AI in finance.
- Leverages the power of open-source for distribution and community building.
Risks
- High build complexity and ongoing maintenance for a solo builder, especially for an open-source project requiring financial-grade reliability.
- Challenging monetization path for an open-source project in a conservative, enterprise-focused industry.
- Competition from well-funded incumbents (Bloomberg, AlphaSense) who offer comprehensive (albeit legacy) solutions.
- Gaining trust and adoption from risk-averse financial institutions for a solo-built, open-source tool is difficult.
- The 'open-source' aspect might devalue the offering for entities that typically pay premium for managed services and guarantees.
Next steps
- Conduct in-depth interviews with target users (quants, analysts using LLMs) to confirm the specific willingness to pay for a dedicated solution, even an open-source one.
- Explore potential commercialization angles beyond pure open-source, such as premium APIs, managed cloud services, or enterprise support plans, to understand revenue potential.
- Validate the minimal viable product (MVP) scope that solves the absolute core pain (e.g., just the token efficiency module) and see if financial institutions would pay for that specific utility.
- Research existing open-source projects in finance to understand their adoption and monetization strategies better.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 18
- Willingness to pay
- 14
- Buildability
- 8
The idea addresses real, specific technical pains for applying AI agents in finance, offering a novel approach to token management and persistence. However, it enters a market with strong, funded incumbents and the build complexity for a solo founder is high, especially for an open-source project.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market pain and growth, but high build complexity and a difficult path to direct monetization and differentiation as an open-source project in a heavily funded market.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Strong problem clarity and niche, but limited creator fit and monetization potential for a solo builder pursuing an open-source model in a complex, high-value industry.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear value proposition for a specific audience, but the open-source business model and the conservative financial industry create significant distribution and monetization risks for a solo builder.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a real, specific pain for a clear user with a promising future, but the immediate commercial wedge and open-source model present challenges for YC's typical product-market fit expectations.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct in-depth interviews with target users (quants, analysts using LLMs) to confirm the specific willingness to pay for a dedicated solution, even an open-source one.
- Explore potential commercialization angles beyond pure open-source, such as premium APIs, managed cloud services, or enterprise support plans, to understand revenue potential.
- Validate the minimal viable product (MVP) scope that solves the absolute core pain (e.g., just the token efficiency module) and see if financial institutions would pay for that specific utility.
- Research existing open-source projects in finance to understand their adoption and monetization strategies better.
Who is already there, emerging market
An AI-powered market intelligence and search platform for financial and business professionals to gain insights from a vast content library, including regulatory filings, research reports, and expert transcripts.
Pricing: Subscription prices vary based on the number of users for small and medium-sized companies, and customized for enterprise-level. Offers a free two-week trial.
A financial data and analytics platform providing real-time financial market intelligence, news, quantitative research, and integrated trading workflows.
Pricing: Approximately $31,980 per user annually (multi-seat discounts apply).
A financial data and analytics platform offering a wide array of financial data, real-time feeds, modeling tools, and document search capabilities.
Pricing: Custom quotes based on users and data entitlements.
A multi-agent AI productivity platform designed to help financial, legal, and corporate teams extract insights from complex, unstructured documents at scale.
Pricing: Not publicly available, likely enterprise-focused with custom quotes.
An open-source investment research platform providing advanced financial data and analysis tools, highly customizable and integrating with various other tools.
Pricing: Free to use and deploy, no subscription fees or user limits.
An AI-driven platform for data-driven forecasting, event impact analysis, and macro scenario modeling, integrated with S&P Global data.
Pricing: Not publicly available, likely enterprise-focused with custom quotes.
A hedge fund that crowdsources market trading from AI programmers over the Internet, allowing data scientists to build machine learning models to predict the stock market.
Pricing: Free to participate as a data scientist, earns cryptocurrency based on model performance. Investment in the hedge fund itself has specific requirements.
- TIKRVisit
An affordable all-in-one tool for long-term investors to conduct deep fundamental research on global stocks, analyst estimates, and news.
Pricing: Free plan for US data, 1 year of analyst estimates, 90 days of company transcripts. Pro plan for advanced functionality and global stocks coverage at a fraction of AlphaSense's price.
A general-purpose AI assistant that excels at document-heavy work like contract analysis and due diligence due to its strong contextual understanding.
Pricing: Starts at $17 per month.
What they charge
Recent news
- Applied AI in Finance Market Size to Hit USD 92.53 Billion by 2035 - Precedence Research
Precedence Research, March 31 2026
- Generative AI In Financial Services Market Size Report, 2030 - Grand View Research
Grand View Research, Not specified in snippet, but likely recent given similar market reports
- AI in Finance Market Size, Demand & Growth by 2034
The Insight Partners, Not specified in snippet, but likely recent given similar market reports
- Best Bloomberg AI Tools to Try in 2026 {VLIZMI}
Medium, April 18 2026
- AlphaSense vs. Bloomberg vs. FactSet Comparison
SourceForge, Not specified in snippet, but updated regularly
Market signals
The applied AI in finance market is large and experiencing significant growth, projected to reach between USD 73.9 billion and USD 92.53 billion by 2035, with CAGRs ranging from 19.5% to 31.30%. Key drivers include increasing demand for automation in BFSI, advanced analytics, enhanced fraud detection, and the rise of fintech startups. North America currently holds the largest market share.
What frustrates people
- Bloomberg's legacy technology, aversion to innovation, and clunky UI/UX.
- AlphaSense's limited visualization tools and collaboration features for users without licenses.
- OpenBB (when compared to a search engine-like tool) requires processing data from a source and displaying it within the platform, which can be perceived as slower than opening a new tab to a data source.
- Bloomberg's high cost and steep learning curve limit accessibility for smaller firms.
- FactSet can be complex for new users and carries a premium cost for advanced modules.
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!
AI
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
AI
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
AI
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