SquadGPT – chat interface to LLMs for teams

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A few months ago, I was working with a couple of interns to build some forgettable CRUD app. Then ChatGPT blew up and when we got stuck on some obscure bug, one of the interns suggested asking ChatGPT Plus for help. Since buying every intern a ChatGPT Plus account wasn’t on the cards, we decided to “solve” this by building a chat interface to the GPT-4 API and allowing multiple people to participate in any chat with the LLM. Eventually, the benefits of this approach became apparent - more visibility into AI costs, I retained access to their chat sessions even after they left the project, better insights into what they needed guidance on, and so on. So, I decided to productise this as SquadGPT - GPT for teams - at https://squadgpt.ai . It’s very barebones - all it does is to let you talk to an LLM (GPT-4) for now. No plug-ins, no web browsing, no REPL. I’ll add those features and others based on what users say. In future, I’m planning to integrate with more LLMs so that this “mixture of experts” can reduce hallucinations and improve reliability. I’m putting out this Show HN because I believe we’ve built something unique in the LLM space and want to get that presumption validated through actual uptake from users.

Visit product View on Hacker NewsSep 24, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, chatgpt · Missing: mac, agents, macos

95%95% 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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, users · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus, interface, users · Missing: platform, intuitive, reviews

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

23%23% 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 · Missing: web3, crypto, cryptocurrency

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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