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Structured Outputs with Deepseek R1

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Structured Outputs with Deepseek R1

This is our interactive playground that uses our framework (BAML https://github.com/BoundaryML/baml ) to do structured outputs with R1, without the use of tool-calling APIs or fine-tuning required. We have a more fleshed out playground at https://promptfiddle.com as well. BAML is a DSL for prompts, where prompts are modeled as functions. Our compiler transforms your LLM function declaration into the relevant API call, and will also parse the output for you. We serialize using "type definitions" instead of using json schemas, since they are more efficient and easier to understand for models. We talk more about why here: https://www.boundaryml.com/blog/type-definition-prompting-ba... We ran the Berkeley Function Calling Benchmark some months ago on our approach and achieved really good results https://www.boundaryml.com/blog/sota-function-calling?q=0

Visit productView on Hacker NewsJan 27, 2025
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
59%59% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: efficient · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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