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Article URL: https://abstreal.substack.com/p/understanding-isnt-necessarily-empathy

Comments URL: https://news.ycombinator.com/item?id=42184552

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Article URL: https://nicholasdecker.substack.com/p/the-returns-to-education

Comments URL: https://news.ycombinator.com/item?id=42184550

Points: 1

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Article URL: https://mon0.substack.com/p/was-it-better-when-we-where-manufacturing

Comments URL: https://news.ycombinator.com/item?id=42184545

Points: 1

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Article URL: https://www.businessinsider.com/india-disposable-clay-tea-cups-compete-single-use-plastic-2024-11

Comments URL: https://news.ycombinator.com/item?id=42184544

Points: 2

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Article URL: https://github.com/raysan5/raylib/releases/tag/5.5

Comments URL: https://news.ycombinator.com/item?id=42184527

Points: 1

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Article URL: https://foundersnotes.substack.com/p/ais-amazon-moment

Comments URL: https://news.ycombinator.com/item?id=42184519

Points: 1

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Article URL: https://www.humblebundle.com/books/math-for-programmers-2024-manning-books

Comments URL: https://news.ycombinator.com/item?id=42184483

Points: 1

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Article URL: https://scitechdaily.com/nasa-captures-black-holes-double-star-destruction-in-epic-collision/

Comments URL: https://news.ycombinator.com/item?id=42184468

Points: 2

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Article URL: https://www.newscientist.com/article/2456653-ai-maths-assistant-could-help-solve-problems-that-humans-are-stuck-on/

Comments URL: https://news.ycombinator.com/item?id=42184463

Points: 1

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Article URL: https://www.nature.com/articles/d41586-024-03614-9

Comments URL: https://news.ycombinator.com/item?id=42184457

Points: 2

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Article URL: https://www.jakeworth.com/posts/take-the-big-project/

Comments URL: https://news.ycombinator.com/item?id=42184456

Points: 1

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Article URL: https://www.redhat.com/en/blog/bringing-red-hat-enterprise-linux-windows-subsystem-linux

Comments URL: https://news.ycombinator.com/item?id=42183791

Points: 1

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Article URL: https://www.tomshardware.com/tech-industry/artificial-intelligence/elon-musks-xai-reportedly-shifts-usd6-billion-ai-server-order-from-troubled-supermicro-to-its-rivals

Comments URL: https://news.ycombinator.com/item?id=42183788

Points: 1

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Article URL: https://colab.research.google.com/github/neuml/txtai/blob/master/examples/67_Whats_new_in_txtai_8_0.ipynb

Comments URL: https://news.ycombinator.com/item?id=42183784

Points: 1

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Article URL: https://models.bggpt.ai/blog/2024-02-18-launching-the-first-free-and-open-bulgarian-llm/

Comments URL: https://news.ycombinator.com/item?id=42183771

Points: 1

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Hi HN! A month ago, I shared LlamaPReview [1] in SHOW HN. Since then, we've grown to 2000+ repos (60%+ public) with 16k+ combined stars. More importantly, we've made significant improvements in both efficiency and review quality.

Key improvements:

1. ReAct-based Review Pipeline We implemented a ReAct (Reasoning + Acting) pattern that mimics how senior developers review code. Here's a simplified version:

```python def react_based_review(pr_context) -> Review: # Step 1: Initial Assessment - Understand the changes initial_analysis = initial_assessment(pr_context) # Step 2: Deep Technical Analysis deep_analysis = deep_analysis(pr_context, initial_analysis) # Step 3: Final Synthesis return synthesize_review(pr_context, initial_analysis, deep_analysis) ``` 2. Two-stage format alignment pipeline

```python def review_pipeline(pr) -> Review: # Stage 1: Deep analysis with large LLM review = react_based_review(pr_context) # Stage 2: Format standardization with small LLM return format_standardize(review) ``` This two-stage approach (large LLM for analysis + small LLM for format standardization) ensures both high-quality insights and consistent output format.

3. Intelligent Skip Analysis We now automatically identify PRs that don't need deep review (docs, dependencies, formatting), reducing token consumption by 40%. Implementation:

```python def intelligent_skip_analysis(pr_changes) -> Tuple[bool, str]: skip_conditions = { 'docs_only': check_documentation_changes, 'dependency_updates': check_dependency_files, 'formatting': check_formatting_only, 'configuration': check_config_files } for condition_name, checker in skip_conditions.items(): if checker(pr_changes): return True, f"Optimizing review: {condition_name}" return False, "Proceeding with full review" ``` Key metrics since launch: - 2000+ repos using LlamaPReview - 60% public, 40% private repositories - 40% reduction in token consumption - 30% faster PR processing - 25% higher user satisfaction

Privacy & Security: Many asked about code privacy in the last thread. Here's how we handle it: - All PR review processing happens in-memory - No permanent storage of repository code - Immediate cleanup after PR review - No training on user code

What's next: We are actively working on GraphRAG-based repository understanding for better in-depth code review analysis and pattern detection.

Links: [1] Previous Show HN discussion: [https://news.ycombinator.com/item?id=41996859] [2] Technical deep-dive: [https://github.com/JetXu-LLM/LlamaPReview-site/discussions/3] [3] Link for Install (free): [https://github.com/marketplace/llamapreview]

Happy to discuss our approach to privacy, technical implementation, or future plans!

Comments URL: https://news.ycombinator.com/item?id=42183760

Points: 1

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Article URL: https://medium.com/@data.scientist/the-ultimate-guide-to-building-an-efficient-llm-in-2024-continuously-updated-ae0e0ab8833f

Comments URL: https://news.ycombinator.com/item?id=42183757

Points: 1

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Article URL: https://practical.engineering/blog/2024/11/14/which-power-plant-does-my-electricity-come-from

Comments URL: https://news.ycombinator.com/item?id=42183747

Points: 1

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Article URL: https://agenttk.kevz.dev/

Comments URL: https://news.ycombinator.com/item?id=42183735

Points: 1

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Article URL: https://www.youtube.com/watch?v=egITnYcUA-I

Comments URL: https://news.ycombinator.com/item?id=42183726

Points: 1

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