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Dealing with Claude 3 XML function calling so you don't have to

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Dealing with Claude 3 XML function calling so you don't have to

Hi all, our open-source Python library for fast, quality development with LLMs has added support for using Anthropic’s Claude 3 models and its XML function calling capabilities, including nested schemas and def/ref resolution. Example: https://github.com/Mirascope/mirascope/blob/main/examples/extracting_tasks_with_claude_haiku.py Repo: https://github.com/Mirascope/mirascope In the example we create a `TaskExtractor` class that can extract a fairly complex, nested `TaskDetails` schema. Haiku does a fantastic job extracting the schema, correctly extracting everything from the task description to due dates to related tasks and metadata. To our knowledge, Mirascope is the first open-source library to support this level of nested schema definition and automatic data extraction with Claude 3. We'd love to hear feedback - have you seen similar capabilities in other libraries? What other use cases do you think this unlocks, especially with the 200K token context limit? We're excited to keep expanding Mirascope's functionality, with streaming tools (function calls) next on the roadmap. Check out the library on GitHub and let us know what you think!

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, models · Missing: mac, agents, macos
82%82% 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 · Strong signals: including · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, io, including · Missing: https docs, just released, exist
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: calls · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
15%15% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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