Every dev with an AI agent is quietly losing hours a day to the same loop: reads the whole repo - misses 2 callers - breaks 3 files - you fix it - repeat. 27,000 files read to change one. Should’ve been 15. 3 MCP servers end it

Noticed this interesting toggle in the Codex app: “Pursue goal”. Looks like agentic loops are quietly becoming standard. Persistent objective, completion criteria, and the agent keeps checking whether it’s actually done. Let’s see what it can get done.

11 DAYS WITH THE #1 CODING MODEL BEFORE IT MOVES BEHIND CREDITS For 1M tokens: Fable 5: $10 input / $50 output GPT-5.5 Pro: $30 input / $180 output And until June 22, Fable 5 is included on Pro, Max, Team, and Enterprise at no extra cost. On June 23, the window closes and usage credits start. Most people will spend the 10 days asking it to rewrite emails. Bug hunters can spend it building a reputation score. The article shows exactly how.

THEY CALLED IT TOO DANGEROUS TO RELEASE. THEY RELEASED IT ANYWAY. Everyone holds the same model that finds the bug now, so the raw find is worth nothing. The money moved to the two things accelerated with the release of Fable 5: > reproduce the bug → prove it's real > sign the report → own the fix Here's where the money actually sits now: > raw AI find → $68–297, high reject, ban risk > verified owned → private invites at 5–30× the rate The vampire-doctor take is true - aim Fable 5 at cyber and it flinches, drops you to a weaker brain. The gating is real now, but limits like this loosen over time. Full playbook in the article.

Okay Jarvis, scan my buddy's X and screenshot that one cringe tweet from 2019 before he deletes it. make no mistakes > a second brain out of the box - your profile, projects, and taste live on your machine, not on someone's server > 300 agents cron running for weeks with you out of the loop > full browser access under your own accounts > deep research by a swarm, each agent on its own clean context So you're telling me I don't have to build and manage my own second brain anymore?? That's wild. It's like Hermes writing skills on the fly while it works - except this one writes the memory itself, and keeps it local?

AI IS COMPRESSING WEEKS OF BUG HUNTING INTO A SINGLE AFTERNOON What separated the top hunters from everyone else was time - the years it took to move fast across recon, review, and disclosure. That gap is closing. One researcher banked $40,000 on a single program using AI to run the pipeline end to end, turning what was a months-long grind into a few focused days.

Your Fable 5 can pay for itself Claude Mythos went public this week as Fable 5 The thing read 1,000 codebases, dug up 23,000 vulns, and wrote 181 working exploits with nobody holding its hand Meanwhile HackerOne quietly paid hunters $81M last year So point it at one in-scope program. It finds the bug and proves it, then writes you the report and the fix. You reproduce it by hand, slap your name on it, get paid The top hunters clear $300k/year, guide below

HackerOne platform cutting a critical payout from $9,250 to $2,257 is the market talking. Raw findings got cheaper. AI made the first draft abundant. Now the platform cares about the work after the draft: Can it reproduce? Is it in scope? Does the impact matter? Can the team ship the fix? That is where a real hunter still wins. The article below breaks down the loop.

Big deal

YOUR AGENT IS WANDERING A CITY WITH NO MAP, KICKING IN EVERY DOOR TO FIND ONE ROOM Ask it to change one function. It greps blindly, reads 40 files, torches the whole context window, and still misses two callers. Now hand it a metro map of your codebase. code-review-graph parses your entire repo into a real graph - every function a station, every call, import and test a line running between them. Touch one file and it instantly traces the blast radius: every caller, every dependent, every test that change hits - and feeds the agent ONLY those stops. 40 files → the exact 4 it actually touches ~8x less context on average, up to 49x on large repos This is the one almost nobody installs - and the one that changes the most. Save this full setup in the article below

YOU HIRE A GENIUS EVERY MORNING AND LOBOTOMIZE HIM EVERY NIGHT Every new chat your agent forgets your architecture, your conventions, that cursed decision in the auth layer. So you re-explain. Again. Every. Single. Session. You're onboarding the same intern on a loop, forever. The fix is dumber than you think: Obsidian = the agent's memory. Free app, everything saved as plain Markdown - models read it natively, no parsing layer. It checks the vault before every task, follows your past calls, and logs a new note each time it cracks something hard Each session makes the next one smarter - while you sleep. Setup: under 10 minutes. Cost: free. Most devs are still copy-pasting context into a box like it's 2023. Save this full setup in the article below

YOU'RE USING ONLY 15% OF YOUR AI MODEL but you can add the other 85% in 3 steps... your agent codes blind because it's missing these things: > map - reads only the 4 files a change actually touches, not 40 (cuts context by 8x and stops it from missing callers) > memory - stores your architecture, conventions, and past bugs in .md files the agent reads every session (no more explaining everything from scratch) > eyes - opens your app in real Chrome, takes screenshots, reads the console (stops it from shipping broken UI and calling it done) I've been wiring this into my Hermes by hand for months, explained below

YOUR AGENT WASTES 60% OF TOKENS READING IRRELEVANT FILES WHEN EDITING Fix that issue with one MCP server Setup is right below

27,000 files read to change one feature. The fix broke 4 things. Read 27,000 again. Fix those. Break more. Repeat. 3 hours and ~2M tokens later, you shipped… nothing. That’s not coding. That’s a token furnace Fix that with the setup below

Honestly, like 90% of the devs I know still write code pretty much by hand. their agent forgets everything between sessions, hallucinates half the output, so they spend the whole day babysitting it instead of shipping. and they think THAT'S the limit of AI coding. it's not even close. here's the thing - the agent itself is actually great. it's just blind, has zero memory, and never sees what it ships. put a human in that seat and he'd hallucinate too. I wrote the EXACT setup that fixes it. 3 MCP servers, one afternoon, and your agent finally gets a map of the codebase, a memory across sessions, and eyes to check its own work. Article below