I stopped using Claude like a chatbot — 7 prompt shifts that reclaimed 10 hours of my week - Tom's Guide
I stopped using Claude like a chatbot — 7 prompt shifts that reclaimed 10 hours of my week Tom's Guide
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93% of a Claude Code Session Is Noise. Here's the Proof.
I wrote a session distiller for Claude Code that cuts 70MB sessions down to 7MB. That post covered the what and how. This one covers the why, with data. Before I wrote a single line of code, I needed to answer three questions: What's actually inside a 70MB session? What's safe to throw away? How do I prove I'm not losing anything that matters? Dissecting a 70MB Session I opened a real 70MB session JSONL and categorized every byte. Here's the breakdown: JSON envelope (sessionId, cwd, version, gitBranch): ~54% Tool results (Read, Bash, Edit, Write, Agent): ~25% Base64 images (screenshots, UI captures): ~12% Thinking blocks (internal reasoning): ~4% Actual conversation text: ~3% Progress lines, file-history-snapshots: ~2% That first line is the surprise. Every single JSONL line repeats the sa
How to Stop Your AI Provider From Holding Your App Hostage
The discourse around who controls AI's future got loud again this week. But while pundits debate trust and governance, I'm staring at a very concrete problem in my codebase: my entire application is hardwired to a single AI provider's API. If they change pricing tomorrow, deprecate a model, or go down for six hours (again), I'm cooked. And if you've built anything with LLM APIs in the last two years, you probably are too. Let's fix that. The Root Cause: Tight Coupling to a Single Provider Here's what most AI integration code looks like in the wild: # This is everywhere. This is the problem. import openai def summarize ( text : str ) -> str : response = openai . chat . completions . create ( model = " gpt-4o " , messages = [{ " role " : " user " , " content " : f " Summarize: { text } " }],
Same Instruction File, Same Score, Completely Different Failures
Two AI coding agents were given the same task with the same 10-rule instruction file. Both scored 70% adherence. Here's the breakdown: Rule Agent A Agent B camelCase variables PASS FAIL No any type FAIL PASS No console.log FAIL PASS Named exports only PASS FAIL Max 300 lines PASS FAIL Test files exist FAIL PASS Agent A had a type safety gap. It used any for request parameters even though it defined the correct types in its own types.ts file. Agent B had a structural discipline gap. It used snake_case for a variable, added a default export following Express conventions over the project rules, and generated a 338-line file by adding features beyond the task scope. Same score. Completely different engineering weaknesses. That table came from RuleProbe . About this case study The comparison us
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Same Instruction File, Same Score, Completely Different Failures
Two AI coding agents were given the same task with the same 10-rule instruction file. Both scored 70% adherence. Here's the breakdown: Rule Agent A Agent B camelCase variables PASS FAIL No any type FAIL PASS No console.log FAIL PASS Named exports only PASS FAIL Max 300 lines PASS FAIL Test files exist FAIL PASS Agent A had a type safety gap. It used any for request parameters even though it defined the correct types in its own types.ts file. Agent B had a structural discipline gap. It used snake_case for a variable, added a default export following Express conventions over the project rules, and generated a 338-line file by adding features beyond the task scope. Same score. Completely different engineering weaknesses. That table came from RuleProbe . About this case study The comparison us
SQUIRE: Interactive UI Authoring via Slot QUery Intermediate REpresentations
Frontend developers create UI prototypes to evaluate alternatives, which is a time-consuming process of repeated iteration and refinement. Generative AI code assistants enable rapid prototyping simply by prompting through a chat interface rather than writing code. However, while this interaction gives developers flexibility since they can write any prompt they wish, it makes it challenging to control what is generated. First, natural language on its own can be ambiguous, making it difficult for developers to precisely communicate their intentions. Second, the model may respond unpredictably…
An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution
In this tutorial, we implement an advanced, practical implementation of the NVIDIA Transformer Engine in Python, focusing on how mixed-precision acceleration can be explored in a realistic deep learning workflow. We set up the environment, verify GPU and CUDA readiness, attempt to install the required Transformer Engine components, and handle compatibility issues gracefully so that [ ] The post An Implementation Guide to Running NVIDIA Transformer Engine with Mixed Precision, FP8 Checks, Benchmarking, and Fallback Execution appeared first on MarkTechPost .
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