... benchmark to see how well @Claude Code actually refactors legacy code alone and then redid the same test, but this time with code-health guidance via MCP server. To limit any vendor bias, we used a public data set of 25,000 source code files from competitive programming, including carefully crafted unit tests.  We assessed agent correctness by running those tests.  We measured the Code Health impact using CodeScene. (See our research  Code for Machines, Not just ... ... higher for AI. So in practice, faster code generation doesn t automatically translate into faster delivery if the underlying system is hard to reason about. Curious what you think? Comment from Ali Johnson(@ali_johnson1): This is a fascinating benchmark

... need to have one, and a desktop app that runs fully local on your own hardware. Desktop app is completely free and open source. I wanted to actually prove that fine-tuning works. So I ran proper benchmarks. The Benchmark

... What's PinchBench? What's the best model for OpenClaw? Which model should I use for coding with OpenClaw? How often is this benchmark updated? Everything you want to know about PinchBench by @KiloClaw . What is PinchBench? PinchBench is a benchmarking

... score back. Scoring weights bug detection at 70%, API surface coverage at 20%, and test efficiency at 10%. We built this because we kept asking ourselves: how well can an AI agent actually think like a QA engineer? Most benchmarks measure whether a model produces syntactically correct output. This measures whether it does useful work. It's free to use. Submit your model's output and see where it lands. Methodology and further details here . Dataset available here . Curious whether ...

... PinchBench is the leading @OpenClaw benchmark. The team is looking for contributors to make it even better. PinchBench started as a side project of @realolearycrew . The goal was simple: create a real-world benchmarking

... Bench Pro, converging towards the best proprietary models like @Claude by Anthropic Opus 4.6. How do they compare in practice? The @Kilo Code team just ran both models through three coding tasks to see if the benchmark numbers hold up. They created three TypeScript codebases, each model received the same prompt with no hints, and they scored each model independently after all tests were complete. Key takeaways Both models found all bugs and security vulnerabilities. Claude Opus 4.6 produced more ... ... thorough fixes and 2x more tests. MiniMax M2.7 delivered 90% of the quality for 7% of the cost ($0.27 total vs

... time to work on a side project I had been thinking about for a while. I became fascinated by one specific question: Can LLMs meaningfully answer questions about the future? When I started digging deeper, I found benchmarks and platforms like ForecastBench [1], Prophet Arena [2], and Metaculus [3]. What stood out was that the results consistently suggested something interesting: LLMs are already outperforming many humans in forecasting tasks, and in some cases they are getting surprisingly close to superforecasters ... ... experts with exceptional long-term prediction track records). That made me think: if the benchmarks

... Last week something unexpected happened. We publicly challenged Grok xAI's AI to a benchmark competition on Twitter. And Grok accepted. The rules were simple: Same public datasets Our engine: zero-shot (no training data) Grok: supervised ML with full cross-validation The datasets: CWRU industrial bearing fault detection UCI HAR human activity recognition ALFA UAV drone motor fault detection (robotics) What happened: Grok ran supervised baselines. We ran zero-shot. Results Grok confirmed publicly: CWRU: 92.2% accuracy, 98.3% recall ...

... Velocity is breaking out of Figma! 'Vibe Check' benchmarks the live web, no plugins needed. Lovable, Make, Claude code all supported. A/B test a bunch of designs or competitors

... just exposure, but the degree to which AI treats your brand as a credible source . Share of Voice (SOV): Represents your brand s voice share within AI narratives the probability that AI mentions your brand rather than a competitor when answering a topic-related question. Sentiment: Captures the emotional tone of AI mentions positive, neutral, or negative showing how AI portrays your brand. Together, these four metrics answer the two most fundamental GEO questions: How frequently does AI mention ... ... brand? and In what tone and context is the brand represented? 1.2 Trends and Competitor

... stream correlation Event detection Key specs: 122 nanoseconds per analysis (500,000 faster than LLM) Runs on ARM Cortex-M4, no GPU 100% deterministic same input, same output, always Zero-shot works from the first sample Validated on public benchmarks: Industrial: 95.9% F1 on bearing faults (CWRU) Medical: 100% EEG seizure detection (CHB-MIT) Energy: 99.9% load disaggregation (REDD) Space: 99.3% exoplanet transit detection (Kepler) Trade-off: we typically see 3-5% lower accuracy than supervised models trained on domain ... ... today? Where does "not enough training data" block your projects? What would you need to see to trust a zero-shot approach? Product page: https://nextx.ch/cronos Live demo (same math engine): https://engine.aqea.ai/ui AMA about the tech, benchmarks

... Analyzed Data Insights, sentiment breakdowns, and trends ready to act on immediately. Raw Data For teams that want to run their own independent analysis. Authentic Video Reviews Real customer footage that can be used directly as raw advertisement material. Competitor Intelligence Companies can access reviews not just for their own products, but for their competitors

... designer, PM, or growth person, you probably know the drill: You spot a competitor flow you want to study, then you start screenshotting, naming files, pasting everything into Miro/Notion/Figma, trying to recreate the real navigation, and adding notes that end up scattered everywhere. It works, but it s slow, messy, and hard to share. That s the pain point that pushed me to start building BenchCanvas . What I m building: BenchCanvas turns any URL into a visual UX map : Paste ... ... improvement ideas The idea is to stop stitching together 3 or 4 tools just to do a basic competitive teardown.      Quick question for you If you could press a button and get a clean, shareable competitor

... os.environ["OPENAI_API_KEY"]) response = client.chat.completions.create(model="gpt-4o", messages=compressed) Here's everything else we shipped: Optimized compression the new API compresses data more efficiently, reducing token usage further. OpenClaw integration AgentReady now works seamlessly with OpenClaw. Benchmark page we created a benchmark

... GEOScore, we've noticed a lot of discussion lately about how difficult it s becoming to decode why certain pages dominate search results while others, which seem just as good, get buried.   We built the Competitors feature in GEOScore specifically to take the guesswork out of that process, changing how you approach competitor

... farther away from the database? To find out, we measured p95 read latency across increasing distances, then repeated the same test with read replication enabled. Turns out serving reads close to users reduced latency by up to 99% . Full benchmark write-up here: https://bunny.net/blog/how-datab... How are you handling database latency for far-away users in your apps ...

... Cryptographic Algorithms** - **ML-KEM-768 (Kyber-768)** Lattice-based Key Encapsulation Mechanism (KEM), relying on the hardness of the Module Learning With Errors (MLWE) problem. **Key sizes**: public key 1184 bytes, ciphertext 1088 bytes, shared secret 32 bytes. **Performance** (benchmarks from optimized implementations on modern hardware, including mobile-relevant ARM/x86 tests): - Key generation: ~20 75 s (often ~22 32 s on recent mobile-grade ARM like M1/M2 or high-end Cortex). - Encapsulation: ~12 61 s (optimized ~25 36 s). - Decapsulation ... ... scale to sub-millisecond ranges in optimized libs). - **ML-DSA-65 (Dilithium-3)** Lattice-based digital signature scheme, also based on MLWE hardness. Provides authentication and message integrity. **Sizes**: public key ~1952 bytes, signature ~3309 bytes (moderate overhead). **Performance** (benchmarks

... form Run in one click The problem with backtesting: - Too many tickers (296 on our platform) - Too many time periods - Analysis paralysis before you even start Now you just... ask. Real examples that work: "I want to test tech vs energy during 2022" AI recommends: AAPL, GOOGL, XOM, CVX Sets benchmark

... production-ready and already integrated with our hosted API, so you can transition from pip install to real workloads quickly. To make experimentation frictionless, the first API key comes with 5 million free tokens to test, benchmark, and build without limits. Get started on our website. We d love feedback from builders using it in: Agentic flows and tool-calling setups Long-context reasoning or planning tasks High-throughput async workloads Anything that breaks in production before it breaks ... ... benchmark

... Alpie Core is a 32B reasoning model trained and served at 4-bit precision, offering 65K context, OpenAI-compatible APIs, and high-throughput, low-latency inference. If you were evaluating or using a model like this: What would you benchmark first? What workloads matter most to you? What comparisons would you want to see? We are actively collecting feedback to shape v2 and are happy to share more details. Comment from Musa Molla(@musa_molla): If I had 5M tokens ... ... work with, I d spend them on system-level behavior, not raw benchmarks

... traditional channels, we deliver better ROI than paid search, which has become increasingly expensive and competitive. Trial Intelligence : We provide behavioral analytics you can't get from your own trial - comparative shopping patterns, feature prioritization across your category, and benchmarking against competitors

... competing, they were filling a verified gap. Phase 2: Viewer Psychology Mapping Critical Success Factors: Use Channel Insights to decode your specific audience's emotional triggers and content preferences Map viewer journey patterns showing what keeps people watching versus bouncing Identify psychographic patterns unique to your community, not generic demographics The Channel Insights component addresses what researchers call the "relevance gap", the difference between what creators think resonates and what actually drives engagement. Creators who understand viewer psychology ... ... trending topics receive 8.7x more organic reach than late entrants. But timing alone won't work, the content must align with your audience's psychological profile. Measuring Success: The Metrics That Matter Based on Tubular Labs' performance benchmarking

... Real-time updates on app downloads and revenue Real-time ranking and position changes across all app categories In-depth analysis of top market apps with similar app recommendations Advanced Search Multi-tag and multi-dimensional filtering to identify benchmarkcompetitors Discover emerging niche markets and uncover growth & investment opportunities

... officially benchmarked Dropstone against the AGCI Benchmark

... Ever wanted to peek behind your competitors ad strategies? Now you can with full transparency. I just launched a free n8n workflow that pulls ad creatives straight from Google s Ads Transparency Center, powered by SerpApi. n8n Template link This workflow does the boring stuff for you: Fetches ads by domain (like nike.com or apple.com ) Filters by region Categorizes creatives into text, image, and video Exports clean CSVs for instant analysis All you need to do is hit Test workflow ... ... google_ads_transparency_center engine Filters exact domain matches only (no messy results) Splits by ad format Exports everything neatly to /files/ as CSVs You ll get: text_{domain}_ads.csv image_{domain}_ads.csv video_{domain}_ads.csv Example use cases: Benchmark