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Cortex Click – LLM-Driven Developer Marketing Platform

Hacker News

Cortex Click – LLM-Driven Developer Marketing Platform

Hey HN! We’re Evan, Derek, Eugene, and Jeremy, the team behind Cortex Click ( https://www.cortexclick.com/ ). Cortex Click is an LLM-driven platform for creating high quality developer marketing content including blog posts, tutorials, documentation, and copilots for product onboarding. You can see a quick demo of the product in action here: https://www.youtube.com/watch?v=mzf_wrnIrYM You can try the product here for free, no credit card required: https://app.cortexclick.com Building a great product is half the battle, and the other half is marketing. We’ve spent our careers working on developer tools, and have learned first hand that communicating and writing about a product is one of the hardest challenges technical founders face. In theory, LLMs are an exciting solution to this problem. But as most readers can probably attest, the LLM writing tools we have today just don’t get the job done. We've written thoughts on the current state of the art and where it falls short: https://www.cortexclick.com/blog/why-do-llm-writing-tools-su... The last thing the world needs is another platform for generating boring, generic, subpar content ("slop" in the parlance of our times). Cortex Click is built for the exact opposite purpose: to give founders, developers, marketers, and their teammates a customized, tunable platform where they can create their best writing. We believe that quality really matters in documentation, blogs, and marketing. To generate the highest quality content possible, we employ a three-pronged strategy: 1. Grounding in your Knowledge Base: Cortex Click searches across your existing developer docs, blog posts, code examples, and other marketing materials to ground content generation, add cross-links, and match brand voice. Further, we automate the process of ingesting your knowledge base, and keeping it up to date so your audience is guaranteed the freshest content. 2. Real-Time Web Research: To fill in any gaps, the workflow performs web research against popular search engines. This augments your knowledge base with access to additional code examples, industry trends, and other facts and statistics. 3. Multi-Agent, Chain of Thought Workflow: All of this context is fed into multiple frontier models from different providers that are used in concert. We leverage the strengths of different models to plan, research, draft, review, and correct the content ultimately leading to a better result than could be achieved with any one model. We started with a markdown editor in the browser for reasons we’re happy to discuss, but we’re already working on IDE integrations (the most commonly requested feature so far). Our backgrounds are primarily in cloud, developer tools, search, information retrieval, and AI. We’re excited to talk shop - in particular anything related to LLM evaluations, regression testing prompt upgrades, and techniques for squeezing every last drop out of frontier models. But most of all, we’d love to hear your feedback on what we’ve built, where it falls short, and ideas on how we could improve your workflow for writing quality developer-focused content.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, including · Missing: supports, reddit linkedin, podcasting
97%97% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, models · Missing: mac, agents, macos
93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, lua · Missing: https docs, just released, open source
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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