Link List

Loop engi­neer­ing is a sys­tem design approach where devel­op­ers build auto­mat­ed recur­sive process­es to prompt AI cod­ing agents rather than man­u­al­ly prompt­ing them each turn. The sys­tem han­dles dis­cov­ery, task assign­ment, ver­i­fi­ca­tion, and iter­a­tion inde­pen­dent­ly, with five key com­po­nents: sched­uled automa­tions, iso­lat­ed work­trees, project knowl­edge doc­u­men­ta­tion, tool con­nec­tors, and sub-agents for check­ing work.

This arti­cle exam­ines ​“prompt debt,” the hid­den costs of rely­ing on hand-tuned nat­ur­al lan­guage prompts to spec­i­fy AI sys­tem behav­ior. It explains how ini­tial pro­to­typ­ing speed masks long-term prob­lems: slow­ing iter­a­tion cycles, team coor­di­na­tion chal­lenges, and mod­el lock-in that pre­vents upgrad­ing to bet­ter alter­na­tives. The author argues that nat­ur­al lan­guage was nev­er designed as an engi­neer­ing spec­i­fi­ca­tion lan­guage and treat­ing it as one fun­da­men­tal­ly lim­its scal­able AI appli­ca­tion devel­op­ment.

Cory Doc­torow dis­cuss­es his new book ​“The Reverse Cen­tau­r’s Guide to Life After AI,” cri­tiquing AI hype and indus­try prac­tices that trans­form humans into ser­vants of machines rather than aug­ment­ing human capa­bil­i­ty. He warns the AI bub­ble is unsus­tain­able and des­tined to col­lapse cat­a­stroph­i­cal­ly, while advo­cat­ing for push­back against nar­ra­tives of AI inevitabil­i­ty.

This arti­cle exam­ines how local lan­guage mod­els became prac­ti­cal­ly viable by mid-2026 through engi­neer­ing inno­va­tions rather than raw para­me­ter scal­ing. Key advances include sparse atten­tion mech­a­nisms that reduce com­pu­ta­tion­al com­plex­i­ty and mix­ture-of-experts archi­tec­tures that main­tain large knowl­edge while acti­vat­ing only a frac­tion of para­me­ters per token. The author dis­cuss­es spe­cif­ic effi­cient mod­els like Qwen 3.6, Gem­ma 4, and DeepSeek V4 that enable prac­ti­cal local deploy­ment for writ­ing, research, and spe­cial­ized tasks.

Open source repos­i­to­ry of plu­g­ins for Claude that enhance pro­duc­tiv­i­ty across knowl­edge work roles includ­ing sales, mar­ket­ing, cus­tomer sup­port, prod­uct man­age­ment, legal, finance, and more. Each plu­g­in inte­grates with pop­u­lar tools like Slack, Notion, Hub­Spot, and Jira to cus­tomize Claude’s capa­bil­i­ties for spe­cif­ic job func­tions and com­pa­ny work­flows.

Warp is an agen­tic devel­op­ment envi­ron­ment, born out of the ter­mi­nal. — warpdotdev/​warp

Anthrop­ic Acad­e­my offers a com­pre­hen­sive suite of cours­es cov­er­ing Claude AI usage, API inte­gra­tion, devel­op­ment work­flows, and AI flu­en­cy across mul­ti­ple domains. Cours­es range from begin­ner tuto­ri­als like Claude 101 to advanced top­ics includ­ing Mod­el Con­text Pro­to­col imple­men­ta­tion and agent skills devel­op­ment. The plat­form uses Skill­jar, a learn­ing man­age­ment sys­tem, to deliv­er inter­ac­tive con­tent, track progress, and pro­vide com­ple­tion cer­tifi­cates.

An open-source, no-code web UI for train­ing, run­ning, and export­ing open mod­els in one uni­fied local inter­face.

Pro­duc­tion-ready Claude Code skills, agents, and project tem­plates for Craft CMS 5 devel­op­ment. Includes tools for plugin/​module devel­op­ment, site devel­op­ment, con­tent mod­el­ing, and Twig tem­plates. Install via Claude Code Plu­g­in mar­ket­place or Ver­cel Skills CLI for auto­mat­ed set­up and con­text-aware assis­tance.

This GitHub page presents code­sight, a uni­ver­sal AI con­text gen­er­a­tor. It saves thou­sands of tokens for AI tools by build­ing a per­sis­tent knowl­edge base direct­ly from your code­base.

The Verge explores how the SEO indus­try manip­u­lates AI search results. Firms cre­ate self-serv­ing ​“best of” lists, caus­ing AI to cite biased prod­uct rec­om­men­da­tions.

Open Brain — The infra­struc­ture lay­er for your think­ing. One data­base, one AI gate­way, one chat chan­nel — any AI plugs in. No mid­dle­ware, no SaaS. — OB1/​skills at main · Nate­B­Jones-Pro­ject­s/OB1

Open Brain — The infra­struc­ture lay­er for your think­ing. One data­base, one AI gate­way, one chat chan­nel — any AI plugs in. No mid­dle­ware, no SaaS. — OB1/​skills at main · Nate­B­Jones-Pro­ject­s/OB1

This prompt kit offers four AI prompts to build and deploy ​“skills” from your best out­puts. It trans­forms per­son­al prompt­ing exper­tise into insti­tu­tion­al method­ol­o­gy for con­sis­tent, scal­able AI use.

This GitHub repo describes Phan­tom, an AI co-work­er with its own com­put­er. It’s self-evolv­ing, has per­sis­tent mem­o­ry, builds tools, and runs on Slack.

This GitHub repo pro­vides best prac­tices for Claude Code. It details struc­tur­ing agents, com­mands, skills, and work­flows to opti­mize AI devel­op­ment.

This course helps web devel­op­ers inte­grate AI into web­sites and apps. It focus­es on plan­ning and respon­si­ble sys­tem design, not spe­cif­ic tools.

Gar­ry Tan’s gstack is a Claude Code set­up on GitHub. It uses 23 AI agents automat­ing roles from CEO to QA for rapid, high-vol­ume soft­ware devel­op­ment.

This page details 15 hid­den and under-uti­lized fea­tures for Claude Code, shared by its cre­ator Boris Cherny. Tips cov­er mobile app use, ses­sion trans­fer, and task automa­tion.

Anthrop­ic improved Claude’s autonomous cod­ing for com­plex app devel­op­ment. A mul­ti-agent sys­tem (gen­er­a­tor, eval­u­a­tor, plan­ner) tack­les long-run­ning task issues like con­text and self-eval­u­a­tion.

Most teams have repet­i­tive, high-vol­ume tasks that eat up their week: migrat­ing com­po­nents, gen­er­at­ing tests, audit­ing con­tent. Night­shift is a batch…

Simon Willison’s fire­side chat cov­ers agen­tic engi­neer­ing. He dis­cuss­es AI adop­tion, trust­ing AI out­put, and how cod­ing agents make test-dri­ven devel­op­ment ​“effec­tive­ly free.”

A Claude Code cheat sheet detail­ing key­board short­cuts, slash com­mands, MCP serv­er man­age­ment, mem­o­ry, and work­flows. Essen­tial for effi­cient use.

Neil Kakkar shares how Claude Code boosts his pro­duc­tiv­i­ty. He auto­mates tasks, speeds work­flows, and del­e­gates to AI agents, becom­ing a ​“man­ag­er of agents.”

GitHub repo: AI agent har­ness opti­miz­er. Enhances Claude Code, Codex, and sim­i­lar AI agents via skills, mem­o­ry, and secu­ri­ty.

AI ​“vibe cod­ing” does­n’t work with vague Eng­lish spec­i­fi­ca­tions. Mas­ter­ing soft­ware com­plex­i­ty requires abstrac­tion, not just AI, which is what cod­ing is.

Expe­ri­enced engi­neers share real-world insights on AI-assist­ed engi­neer­ing. Learn prac­ti­cal appli­ca­tions for mul­ti-repo man­age­ment and code reviews.

This guide details tech­niques to reduce hal­lu­ci­na­tions in Claude API out­puts. It cov­ers basic strate­gies (e.g., ​“I don’t know”) and advanced ver­i­fi­ca­tion meth­ods.

Stitch is an AI-pow­ered design tool that helps users cre­ate visu­als and graph­ics. It lever­ages arti­fi­cial intel­li­gence to stream­line the design process.

Simon Willi­son explains how AI cod­ing agents improve code qual­i­ty. They han­dle refac­tor­ing, reduce tech­ni­cal debt, and assist pro­to­typ­ing, lead­ing to bet­ter code.