#performance regression
268 related articles
·3 min
Wattage: An AI Agent Token Cost Profiling and Regression Protection Tool
Deep dive into Wattage, an AI Agent token consumption profiling and cost regression protection tool, exploring its core features, industry context, and value for developers.
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·2 min
D-Flash: How Diffusion Drafting Delivers 3.5x Faster LLM Inference
D-Flash solves the autoregressive drafter latency bottleneck in speculative decoding via fast diffusion parallel drafting and target feature KV injection. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, beating EAGLE3 and MTP.
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·3 min
D-Flash: The Secret Behind 3.5x LLM Inference Speedup with Diffusion Drafting
D-Flash uses fast diffusion parallel drafting and target feature KV injection to solve the autoregressive Drafter latency bottleneck in speculative decoding. 16 tokens in just 6ms, up to 3.5x speedup on HumanEval, surpassing EAGLE3 and MTP.
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·3 min
Stop Flying Blind: A Complete Practical Guide to Writing Evals for AI Agent Skills
A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. This guide covers skill descriptions, test design, eval harnesses, and retirement strategies.
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·3 min
Stop Flying Blind: A Complete Practical Guide to Writing Evals for AI Agent Skills
A Google DeepMind engineer reveals that over 50,000 AI agent skills come with almost no evals. Learn how to write skill evaluations, from description tuning to test design.
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·1 min
Agent Device: Let AI Verify Its Own App After Writing the Code
Agent Device is an open-source tool from CallStack that lets AI truly "understand" interfaces via structured accessibility snapshots, closing the loop of open, observe, operate, and verify across iOS, Android, and React Native.
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·8 min
A Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
In-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing and the transition path for test engineers.
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·8 min
The Complete Guide to Agent Testing: Five Core Dimensions and Automation in Practice
An in-depth analysis of the five core dimensions of AI Agent testing: command safety, tool-calling accuracy, task planning, output consistency, and error self-repair. Master automated testing methods and the transition path for test engineers.
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sqlite-utils 4.1 Released: The --code …
·7 min
sqlite-utils 4.1 Released: The --code Option, Type Overrides, and AI-Assisted Development in Practice
sqlite-utils 4.1 brings practical new features: the --code option for generating rows via Python, type overrides to solve ZIP-code leading-zero loss, STRICT table mode switching, drop-index, and more. A deep dive into the design and AI-assisted dev workflow.
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sqlite-utils 4.1 Released: The --code …
·7 min
sqlite-utils 4.1 Released: The --code Option, Type Overrides, and AI-Assisted Development in Practice
sqlite-utils 4.1 brings practical new features: the --code option for generating rows with Python, field type overrides fixing leading-zero ZIP codes, STRICT table mode switching, drop-index, and more. A deep dive into each design and the AI-assisted dev workflow.
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Intelligent Model Routing: The Core Te…
·3 min
Intelligent Model Routing: The Core Technology and Implementation Guide for AI Cost Reduction and Efficiency
Intelligent Model Routing is becoming key AI infrastructure. This article explores its principles, solution types, technical challenges, and implementation considerations to help developers balance cost, latency, and quality.
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·3 min
Is Gemini Getting Worse? A Deep Dive into the 'Model Degradation' Phenomenon
Reddit users report Gemini feeling "worse over time." This deep dive explains the real causes—silent updates, safety over-alignment, and compute throttling—plus how to rationally evaluate AI model performance.
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Kronos Financial Foundation Model: Usi…
·7 min
Kronos Financial Foundation Model: Using AI to Read the Market Language of Candlesticks
Kronos is the first open-source foundation model treating candlestick data as the "language of financial markets," using an autoregressive Transformer and earning 32K GitHub Stars. A deep dive into its principles, applications, and limits.
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·3 min
Qwen3.8 Deep Dive: How Does a 2.4 Trillion Parameter MoE Model Claim Second Place?
Alibaba's Qwen3.8 challenges larger models with a 2.4T-parameter MoE architecture, claiming second only to Gemini. A deep dive into MoE mechanics, continuous updates, two-speed release strategy, and real local deployment requirements.
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·4 min
Was Claude Nerfed After Coming Back Online? The Truth Behind Safety Classifier Routing
Did Claude drop ~10 benchmark points after redeployment? We dig into the safety classifier routing mechanism, Arena voting data, and developer feedback to reveal the truth.
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·7 min
AI Daily: T-Head Open-Sources SAIL Software Stack at WAIC, Flurry of Large Model Releases
T-Head open-sources AI software stack T-Head SAIL at WAIC to lower the barrier for domestic chip development; Kimi K3 tops the WebDev leaderboard; Qwen 3.8 Max Preview cuts prices aggressively; Moonshot prepares a Hong Kong IPO; and Oracle switches its data center to a fuel cell microgrid.
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·3 min
Three Major Chinese AI Models Incoming: Qwen 4, DeepSeek V4, and GLM Next Version Breakdown
Alibaba Qwen 4, DeepSeek V4, and Zhipu GLM's next-gen models are all nearing release. A deep dive into the latest leaks, capability improvements, and timelines for these three Chinese AI flagships.
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·2 min
Ego Vision: A Deep Dive into the YOLO11-Based Driving Action Prediction System
Ego Vision is an open-source autonomous driving perception project integrating YOLO11, ByteTrack, and Depth Anything V2 to predict GO/SLOW DOWN/STOP/EMERGENCY BRAKE actions.
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·3 min
Claude Sonnet 5 Deep Dive: Impressive Benchmarks, Disappointing Reality — Stick with OPUS 4.8
Anthropic's Claude Sonnet 5 claims near-OPUS 4.8 performance at lower cost. Real-world tests reveal hidden tokenizer costs, weak creative output, and only 13th place on Cursor rankings.
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·6 min
The Evolution of Claude Code: From Manual Review to 65% Fully-Automated PRs
Anthropic engineers reveal Claude Code's 18-month evolution: system prompt cut by 80%, 65% of PRs shipped automatically by AI, Claude Tag collaboration, and the safety logic behind auto mode.
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