In partnership with
Hey builders,
A coding agent cannot fix what it has not found. Before it reads half a repo—or confidently edits the wrong file—make it hand you a small evidence trail.
Start here (5 min): pick one harmless repo question, then make a verified location packet ↓. Use the included assets/LOCATION-PACKET.md to capture what you checked. No paid model, API key or code upload is required.
📦 From Our Partners
1,000+ Proven ChatGPT Prompts That Help You Work 10X Faster
ChatGPT is insanely powerful.
But most people waste 90% of its potential by using it like Google.
These 1,000+ proven ChatGPT prompts fix that and help you work 10X faster.
Sign up for Superhuman AI and get:
1,000+ ready-to-use prompts to solve problems in minutes instead of hours—tested & used by 1M+ professionals
Superhuman AI newsletter (3 min daily) so you keep learning new AI tools & tutorials to stay ahead in your career—the prompts are just the beginning
100+ ChatGPT Prompts to Revolutionize Your Day
Supercharge your productivity with HubSpot's comprehensive guide to Chat GPT. This free resource is your fast track to AI mastery:
Industry-Specific Use Cases: 15+ real-world applications across various sectors
Productivity Guide: 21 best practices to 10x your efficiency with AI
Prompt Powerhouse: 100+ ready-to-use prompts for immediate implementation
Challenge Buster: Overcome common AI hurdles with expert strategies
Plus, in-depth sections on email composition, content creation, customer support, and data analysis.
🔎 THE ONE THING
ColGrep-Agent-2B searches code locally before a larger agent burns context →. LightOn reports that, across 52 code-search questions run three times in four setups, its local 2B search agent used 34.3% fewer frontier-model tokens than grep alone. That is a useful claim to test—not a reason to skip review.
The better lesson is smaller: a model should return file paths, line ranges and evidence before it proposes a patch. You can practice that with plain Git search today. The optional local agent comes later.
My take: code search is where agents lose the plot quietly. A tiny location packet makes the handoff inspectable, works without a subscription and catches the expensive mistake: treating the first plausible file as the answer.
Hype Scale: 🔨🔨 (2/5) — local semantic search is useful; one vendor’s token study is not your codebase benchmark.
⚡ THE 5-MINUTE MOVE
Make a location packet before asking for a fix.
Choose a safe repository. Use a public project, a toy repo or code you are authorized to inspect. Do not paste secrets into any chat.
Write one behavior question. Try: “Where is the retry limit chosen after a failed upload?” Not: “Fix uploads.”
Start with two concrete clues. Run a literal search for an error string, config key or function name:
git grep -n -i -e "retry" -e "upload" -- .Open, don’t assume. Read the surrounding lines for the best three hits. Record the path, line range, why it matters and one thing that could disprove it in the Location Packet.
Ask for a read-only answer. Use the prompt below with only approved snippets. Check every cited line yourself before anyone writes or runs a patch.
The rule: search output is a lead; a reviewed file is evidence.
📋 THE LOCATION PACKET
Use this with any free chat after you have gathered approved, non-sensitive snippets. Or complete assets/LOCATION-PACKET.md without a model at all.
You are a read-only codebase locator. Do not write, run or suggest a patch.
BEHAVIOR QUESTION: [what should happen / where it may be decided]
OBSERVED CLUE: [error text, config key, symbol or test name]
CANDIDATE SNIPPETS: [path + line range + only approved code]
Return exactly:
1. A one-sentence hypothesis, marked CONFIRMED, PLAUSIBLE or UNKNOWN
2. Up to three locations: path, line range and why each matters
3. One counter-evidence check for the top location
4. The next read-only file or test to inspect
5. A stop rule: when a human must review before any change
If the snippets do not prove the path, write NEED MORE EVIDENCE.
Never invent files, line numbers, runtime behavior or permissions.Check: a good packet names evidence and counter-evidence. It cannot prove a fix is correct, authorized, secure or safe to deploy.
🧪 THE OPTIONAL LOCAL AGENT
ColGrep is a free, local semantic code-search tool; its optional --agent mode downloads a roughly 2.5GB model, needs no external API key and uses your GPU when available or CPU otherwise. It is an optional accelerator—not today’s requirement.
# macOS / Linux
brew install lightonai/tap/colgrep && colgrep --install-agent
cd /path/to/approved-project
colgrep init
colgrep --agent "Where is the retry limit chosen after a failed upload?"The official project also publishes a PowerShell installer for Windows and a source/Cargo route. Keep the output read-only. Its code-search benchmark used a paid frontier coding agent in the comparison; your local index does not grant that agent, improve every task or replace human review.
🎁 THE EVIDENCE BENCH
🔎 ONE TAB FOR MONDAY
NVIDIA has open-sourced Run:ai’s GPU orchestration platform → — a production infrastructure move, not a weekend install. It is relevant when one local experiment becomes a shared GPU queue with quotas, scheduling and operating rules.
📐 ONE TERM A DAY
Today’s term: code localization
Plain English: Finding the exact shelves that answer a question before you try to rewrite the library.
Why you should care: Narrow evidence makes a coding agent cheaper to steer—and easier to catch when it is wrong.
🎁 FREE SUPPLY DROP
1,000 Powerful Prompts for Claude AI — a prompt pack covering writing, coding, research and business work, so you stop starting from a blank box.
💬 Reply PATH with the repo question you always ask before a change.
Tools Down
Swati, ByteBuilders