Claude vs. GPT Agent Comparison

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Anthropic's recent announcement of tool use/function calls caught my attention, specifically their claim that the Claude models can correctly handle 250+ tools with >90% accuracy. I've been working with GPT function calling for a while and noticed that the recall for larger and more complex functions is quite low. So, I decided to compare GPT and Claude's performance in using different tools for tasks like web scraping and browser automation. Learnings: - AI agents still work best for simple, well-constrained tasks. - To create a successful agent, you need to provide it with good tools. The LLM can then figure out the correct sequence of tool calls itself, which feel like a promising direction. - Tool use is still quite slow and often very expensive. I've spend around $50 just on experimenting with Claude for one day. Imagine what the testing would cost for a production-scale system. Making the unit economics work is difficult but will improve as LLM costs continue to drop.

Visit product View on Hacker NewsApr 11, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor

96%96% 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

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, 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

42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive

41%41% 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

21%21% 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.

Correct prediction on native model

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