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Windsurf – Agentic IDE

Hacker News

Windsurf – Agentic IDE

At Codeium, we've been building AI-powered extensions for a while (we first launched our VSCode extension with autocomplete a little over 2 years ago!), but we've always thought there would come a day where we would hit the limits of what could be achieved within existing IDEs, so we decided to build our own: Windsurf (yes, it's yet another VSCode fork :) We've stuffed a lot of cool features into Windsurf—a super fast autocomplete model, an inline diff generation experience that feels truly native, but we're most proud of Cascade, which is an evolution of the sidebar chat experience that many other extensions have. Cascade can perform deep reasoning on your existing codebase, access a vast array of tools that allow it to run terminal commands and find relevant files, and it's omniscient of all the actions that the user has taken independent of invoking the AI. (You can for example, start implementing a change manually and just ask Cascade to "continue"). We've been using Cascade internally at Codeium on our actual production codebase, and we're getting actual value from it. We hope everyone here does too! You can find a bunch of demos of Cascade on our website but I want to show one that I made myself using Cascade to solve an interesting cryptography challenge: https://youtu.be/LbYepFmVB20 Cascade was able to explain the problem to me, install some libraries needed to interact with the challenge, give me some pointers towards a solution, and implement an attack that I described to it all by itself.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, agentic · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: way · Missing: mobile apps, ios, personal
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, crypto · Missing: web3, cryptocurrency, make money
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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