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Time Machine for Stock market intel
Agentic way to crowd source market intelligence at scale
Every day, millions of people ask Claude, GPT, Gemini, and local models questions about companies and markets. Great insights are generated. Tomorrow, someone else asks the same questions and the whole process starts again. It feels wasteful. Software engineers solved this problem decades ago with version control, collaboration, and open source. Instead of one giant AI model • Claude • GPT • Gemini • DeepSeek • Local models • LangGraph agents • Custom pipelines
Hi everyone,
I built Open Source Alpha(https://rahiakil.github.io/agent...) because I kept noticing something that felt fundamentally wasteful.
Every day, millions of people ask Claude, GPT, Gemini, DeepSeek, and local models questions about companies and markets. Valuable insights are generated, used once, and then disappear forever inside a context window.
Tomorrow, someone else asks the same questions, and the entire process starts over again.
Software engineers solved this problem decades ago with version control and open collaboration. So I started wondering:
What if market intelligence worked the same way?
Open Source Alpha is an experiment in collective intelligence.
Anyone can contribute using whatever tools they prefer. Some people may use Claude, others GPT, Gemini, local models, LangGraph agents, or custom workflows. Contributors spend only a small amount of their own tokens, summarize what they discover, and submit their findings through GitHub pull requests.
Over time, thousands of these small contributions accumulate into something much larger than any one person or model could create alone.
The goal is not to predict tomorrow's stock price.
The goal is to build a long-term memory for markets.
A place where reasoning, observations, earnings summaries, industry trends, and bullish and bearish arguments are preserved instead of forgotten.
Because contributors use different models and approaches, diversity becomes a strength. And as independent analyses begin to agree, consensus naturally emerges.
In many ways, I think of it as:
Wikipedia × GitHub × AI Agents × Collective Intelligence.
History compounds.
Memory compounds.
Perhaps intelligence should compound too.
I'd love to hear your thoughts, suggestions, and criticisms. I'm particularly interested in hearing how people think contributor reputation, consensus, and long-term historical accuracy should be handled.
Thank you for checking it out. https://rahiakil.github.io/agent...
About Time Machine for Stock market intel on Product Hunt
“Agentic way to crowd source market intelligence at scale”
Time Machine for Stock market intel was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #75 on the daily leaderboard. Every day, millions of people ask Claude, GPT, Gemini, and local models questions about companies and markets. Great insights are generated. Tomorrow, someone else asks the same questions and the whole process starts again. It feels wasteful. Software engineers solved this problem decades ago with version control, collaboration, and open source. Instead of one giant AI model • Claude • GPT • Gemini • DeepSeek • Local models • LangGraph agents • Custom pipelines
On the analytics side, Time Machine for Stock market intel competes within Open Source, Analytics and GitHub — topics that collectively have 282.1k followers on Product Hunt. The dashboard above tracks how Time Machine for Stock market intel performed against the three products that launched closest to it on the same day.
Who hunted Time Machine for Stock market intel?
Time Machine for Stock market intel was hunted by Agentic Silicon Photonics. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Time Machine for Stock market intel including community comment highlights and product details, visit the product overview.
Agentic Silicon Photonics