SataEric

Product Designer @wyloapp | UX Designer → Design Engineer & Framer Expert | Follow me → And get my paid framer templates for free.

[SataEric @SataEricUX]

My New E-commerce Template Is Live on the @framer
Marketplace!

📷Like this post
💬 Comment "Template"
📷Repost

And I’ll send the link directly to your DMs.

[José Siles | AI | Data @josesilesdata]

PÍDELE A CLAUDE QUE MAPEE TODA LA ARQUITECTURA DE TU APP EN UNA SOLA PÁGINA HTML Y UN ARCHIVO JSON.

El HTML es para ti.

El JSON es para el siguiente agente que trabaje en una nueva feature.

Tu codebase ahora se explica solo 👇🏻

[HackerNoon | Learn Any Technology @hackernoon]

The command line is evolving beyond a developer utility.

This engineering case study explores how one internal CLI grew into a powerful team tool, accelerating workflows and enabling AI agents to use the same interfaces as developers:

[Kenny1st @0xKenny1st]

CLAUDE CAN NOW BUILD THE KIND OF WEBSITE THAT USUALLY COSTS $3,000+, FROM JUST A DESCRIPTION, NOT A TEMPLATE

no drag-and-drop builder, no theme marketplace, no dev handoff, just a conversation

tell it what the business does, who it's for, what tone it should hit, and Claude writes the copy, lays out the sections, and ships working code in the same thread

the part people miss: the first draft is never the final draft, the real leverage is that revisions take one sentence instead of a support ticket to a freelancer

"make the hero section darker," "add a pricing table," "this feels too corporate, loosen it up" each one lands in seconds, not days

what used to be a week of back-and-forth with a designer is now an afternoon of talking to Claude until it feels right

the tool isn't the moat anymore, knowing what to ask for is

[Carl Pei @getpeid]

New product

[Akshay 🚀 @akshay_pachaar]

from prompt → context → harness → loop → graph engineering.

the list keeps growing, and every new term gets treated as a replacement for the last one. each layer wraps the one before it, and the cleanest way to tell them apart is to ask what a single unit of work looks like.

𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗲𝘀𝘀𝗮𝗴𝗲.

the model remembers nothing before this call, so the prompt has to carry the full universe of what it needs. a role, the background, the instructions, a few examples, a format.

when the output falls short, the skill is working out which ingredient let you down, not rewriting the instructions every time.

the unit of work is one input.

𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗲𝗺𝗼𝗿𝘆.

across many steps the window is finite and the available information is not, which forces a curation step. a curator keeps what matters, compresses what is useful but bulky, and drops the rest.

good curation is mostly about knowing what to throw away, not packing more in.

the unit of work is what stays in the window.

𝗵𝗮𝗿𝗻𝗲𝘀𝘀 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗺𝗮𝗰𝗵𝗶𝗻𝗲.

on its own a model just generates text. the harness gathers what it needs, runs it, calls tools or sub-agents, and verifies the result with tests or a judge.

that verify step is the entire difference between calling an api and running an agent.

the unit of work is one pass through the machine.

𝗹𝗼𝗼𝗽 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗿𝘂𝗻.

one pass rarely finishes the job, so something has to decide whether to run the machine again. that decision needs a goal defined upfront, brakes like max iterations and budget caps, and a completion check that is automated rather than felt.

an agent that stops asking for tools has ended its turn, which is not the same as finishing the task.

the unit of work is the whole run.

the diagram covers those four. here is the fifth.

𝗴𝗿𝗮𝗽𝗵 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻.

once several loops have to work together, you need to say what runs when, what runs in parallel, and who checks whom. nodes do the work, edges decide what runs next, and shared state flows between them.

a single loop is just a one-node graph with an edge pointing back at itself, which is why graphs govern loops instead of replacing them.

the unit of work is the whole job.

here is the part that ties it together.

prompt and context both live inside the harness gather step. the harness is one pass, the loop decides whether to run that pass again, and the graph decides which loops run at all.

zoom out and the unit of work gets bigger. zoom in and you are back at the prompt.

that also tells you where to debug. find the layer whose unit of work broke, then fix that layer.

the prompt is the easiest layer to edit, which is why it keeps taking the blame for failures that live three layers up.

i also published this deep dive on graph engineering, covering the core idea, how to get started, shared state, routing you can trust, and when a graph is genuinely overkill.

the article is quoted below.

[Sha @its_sslvr]

cosmic+wind+

[Edoardo Lunardi @edo_lunardi]

Can't stop playing with this ASCII page transition

[Codez @0xCodez]

Anthropic engineer just dropped a 2-hour workshop on “Graph Engineering” for agentic systems:

“80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.”

• 04:43 - Introduction to RAG and graphs
• 26:30 - Core of graph engineering (nodes, edges)
• 01:09:11 - Agents data indexing in Graphs
• 01:30:50 - Three layers of Graphs for agents
• 01:48:15 - Adaptive RAG for self-verification
• 02:16:37 - The future of graph engineering

This 2-hour workshop will replace your $500 agentic memory course.

Watch it today, then read how to become a graph engineer in the article below.

[Vladimir Bayandin @vovudebosh]

🚨 NO CLIENTS? 6 MONTHS BUILDING, STILL $0 REVENUE.

THE PROBLEM WAS NEVER THE PRODUCT — IT WAS SALES.

DROP YOUR LINK: THIS AI FINDS YOUR BUYERS, WRITES THE EMAILS, BOOKS THE CALLS.

$30 FREE CREDITS, ONLY 9 LEFT — GONE TONIGHT 👇

https://t.co/zIoFBagUPx

[Sprytix @Sprytixl]

ANTHROPIC'S LEAD ENGINEER LEAKED A $1.2M SYSTEM THAT TURNS ONE ENGINEER INTO A FLEET OF 10 LOOPS

your hands were the bottleneck all along - here are 8 steps that change that

stop writing, start delegating - loop: feature, loop: bugfix, loop: tests - each runs independently and ships in parallel

CLAUDE.md is law - every agent reads one file before acting - ten agents, one voice, zero chaos

Opus 5 breaks the task into 3 independent subtasks - a bad plan multiplied by 10 workers equals 10 disasters

each agent gets an isolated git worktree - three features land in the repo while you review one

crash at hour 3 means resume at hour 3 not zero - without this you lose hours every day

every loop writes its findings to the shared graph - worker 3 never repeats worker 1's dead end

at night CLAUDE.md patches itself based on failures - tomorrow the fleet wakes up smarter

save this and paste into Claude Code - and your first fleet of 10 loops is already running

[angel @angeldot_]

UN EQUIPO ACABA DE DESPLEGAR 15 AGENTES AUTÓNOMOS EN LOOP A PARTIR DE UN SOLO PROMPT USANDO GRAPH ENGINEERING

La mayoría de desarrolladores todavía crean sistemas multiagente a mano, escribiendo lógica independiente para cada tarea.

Con Graph Engineering, eso cambia por completo.

Un graph central define la arquitectura y despliega los 15 agentes simultáneamente.

Un único prompt de apenas 200 palabras genera toda la estructura y enruta 120 conexiones entre agentes de forma automática.

En lugar de romperse cuando reciben instrucciones contradictorias, los agentes se corrigen entre sí gracias a un estado compartido que se actualiza continuamente.

Gestionar una malla de 15 nodos exige un alto rendimiento y una API con baja latencia, ya que todos los agentes intercambian contexto en tiempo real.

Mira el vídeo y luego aprende como crear tus propios grafos con el artículo de abajo

Así es como funciona esta arquitectura multiagente basada en grafos y automatizada en tiempo real ↓

[Om Patel @om_patel5]

CLAUDE HAS A SERIOUS PRIVACY PROBLEM RIGHT NOW, A HUGE NUMBER OF SHARED CONVERSATIONS ARE PUBLICLY INDEXED ON GOOGLE FOR ANYONE TO FIND

when you use claude's share feature it makes a public link. it turns out those links got indexed by search engines, so "share with anyone who has the link" actually became "anyone can find this by searching"

and people are pulling up genuinely alarming stuff:

> api keys, credentials and crypto wallets
> personal resumes with real names, addresses and phone numbers
> a lawyer working through a potential ethics violation
> an engineers internal company project details
> what appear to be peoples social security numbers
> and a crazy number of deeply personal chats people never imagined another human would read

anthropic never added a noindex tag to those shared pages, so search engines were free to crawl and list them

one line of code would have prevented the whole thing

this already happened to chatgpt about a year ago, same exact issue, but openai patched it fast

if you have EVER hit share on a claude chat, assume it could be public

go to settings > privacy > your data > shared chats > manage

delete anything you dont want the whole internet to see, especially anything personal or financial

[James Shields @scaling_shields]

met a guy making $51,000/month by scraping reddit for the phrase "anyone got recommendations for" and emailing the posters within 2 hours

not joking

he opens reddit every morning
runs a search on 8 subreddits in his niche
finds people who just publicly asked for exactly what he sells
and emails them while the post is still on the front page

reply rate: 23%

average cold email: 0.3%

his is 76x higher

because these people literally just raised their hand in public saying "please take my money"

heres the exact phrases he searches every morning:

"anyone got recommendations for [service]"
"alternative to [competitor]"
"looking for a good [service]"
"got burned by [company], where do i go now"
"has anyone used [company] — are they worth it"

every one of these is a public declaration of buying intent with a timestamp on it

someone posted this 4 hours ago in r/[niche]:

"I need [service] urgently. Budget is $15K. Prefer someone who actually knows what theyre doing and isnt sketchy"

he saw the post at 9am
pulled the username
cross referenced linkedin
found the email
sent this at 10am:

"hey [name], saw your post in r/[sub] about needing [service] urgently. we do exactly that — last client we booked 34 qualified calls in a month. free 15 min call?"

reply at 10:11am
call at 2pm
signed $15K contract by 4pm

7 hours from public reddit post to signed deal

from reading reddit in the morning

this is the process:

step 1: find the 5-10 subreddits where your buyers hang out

step 2: run searches for "recommendation" "alternative to" "looking for" "anyone use" in those subreddits every morning

step 3: filter for posts under 4 hours old with clear buying intent

step 4: pull the username → cross reference linkedin → find their email

step 5: send a 2 line email referencing their EXACT post

step 6: warm call every positive reply within 30 minutes

thats it

last month:

- 187 reddit posts scraped
- 164 emails found (90% find rate)
- 38 positive replies
- 24 booked calls
- 11 closes
- $4,600 average deal
- $50,600 in revenue

from reading reddit while eating breakfast

heres why this layers perfectly on top of a normal cold email system:

your main campaigns are emailing 4,500 cold leads a day building pipeline at scale

reddit is a surgical strike on top of that

the guy on reddit literally JUST declared their problem hours ago

the declaration is the qualifier

theres no discovering pain points

they posted the problem
you showed up with the solution
the sales conversation is already 90% done before the call starts

its the highest intent cold email you can send because it might not even feel cold

they had the problem at 9am
you showed up at 10am
thats not a cold email thats a well timed introduction

and heres the part that should make every cold emailer sick:

every single person on this app is scraping apollo fighting over the same 50 million contacts

meanwhile reddit has 500 million monthly users posting their exact buying requests in public

timestamped
organised by niche
completely uncontested
free

nobody in cold email is scraping reddit

because nobody thinks of reddit as a lead source

they think of it as a place to post memes

while angry buyers are literally typing their credit card out loud every single day

one of the most profitable lead source on the internet has been sitting there for 15 years

organising itself
timestamping itself
qualifying itself
telling you their budget
telling you their pain
telling you what they just got burned by

and agency owners are still writing "hey firstname hope this finds you well" to marketing directors on apollo

go open reddit

search one of those phrases in a subreddit in your niche

youll find 10 hand raisers before your coffee gets cold

simples

p.s. if youre an agency owner with a proven offer and want us to setup a cold email system that books you 10-30 calls per month - DM me "EMAIL"

(you ONLY pay for qualified calls actually booked onto your calendar)

[Tran Mau Tri Tam ✪ @tranmautritam]

I ignored my health for years while designing. Built a Mac app to fix it.

Ding! is a tiny reminder that tells me to drink and stand up. Made it on @glazeapp because I needed it, releasing it because you might too.

Award entry, try it and support me with an upvote 👇

[Sprytix @Sprytixl]

ANTHROPIC'S LEAD ENGINEER WON A $1.2M BONUS FOR A SYSTEM THAT TURNS ANY DATA CHAOS INTO A GRAPH IN 8 STEPS

raw chaos in - self-updating graph out - and the agent gets +42% productivity from day one

Load → Extract → Graph → Index → Query → Memory → Swarm → Update

eight steps, one pipeline, graph grows while you sleep

documents, code, Slack - everything into one stream through Claude Code - nothing gets lost

Fable 5 + Opus 5 extracts entity relationships - Neo4j builds a live structure - zero duplicates

three types of search in one answer - vectors, keywords and graph - merged ranking gives accuracy no RAG delivers alone

a nightly agent pulls new data and updates the graph automatically - the system gets smarter while you sleep

bookmark and paste into Claude Code - a $1.2M system now free

[Thariq @trq212]

We removed ~80% of the Claude Code system prompt for our newest models, this is what we've learned about writing system prompts, skills and Claude.MDs for them. https://t.co/6DZwSrZjE9

[Jakub Antalik @Jakubantalik]

Thinking Orbs, animated component library

6 types, optimized for large and small sizes, light & dark mode, playground, zero dependencies

https://t.co/PdGxwi6Kws
npm install thinking-orbs

Collaborated on this with @a_brinza who did a lot of work

[Hanako @hanakoxbt]

10 agent evals for AI engineers:

(explained with usage)

1) golden set
→ a fixed set of cases you never edit, run on every single change.
→ use as the baseline that tells you whether anything moved at all.

2) llm as judge
→ a second model scores the output against a written rubric.
→ use when the answer is open-ended and there is no string to match against.

3) rubric scoring
→ one number per dimension: correctness, tone, safety, cost.
→ use when a single score hides which part actually got worse.

4) trajectory eval
→ grade the path the agent took, not only the answer it landed on.
→ use when the right answer for the wrong reason is going to bite you later.

5) tool unit tests
→ test each tool on its own, with fixtures, no model in the loop.
→ use always. most agent bugs are tool bugs wearing a costume.

6) regression suite
→ replay past runs against the new prompt or model and diff the results.
→ use before every prompt change, because prompts have no type system.

7) a/b in prod
→ split live traffic between two versions and compare outcomes, not vibes.
→ use when offline scores stopped predicting what users actually do.

8) human review
→ sample a slice of runs and have a person grade them honestly.
→ use to calibrate your judge, because a judge nobody checks quietly drifts.

9) shadow run
→ the candidate runs on real traffic in parallel and its output is shown to nobody.
→ use before a risky rollout, when one bad answer would be expensive.

10) red team
→ deliberately attack it: jailbreaks, injection, exfil, tool abuse.
→ use before anyone external can reach it, not after.

offline evals tell you it works. online evals tell you it still works.

both sides matter, but not all ten do. run the two that would have caught your last outage.

save this. then read the full breakdown on loop engineering below.

[Hanako @hanakoxbt]

your agent loop needs 8 exits. most people ship only one.

(explained with triggers)

1) goal met
→ an evaluator scores the output against a rubric, and the run stops on a pass.
→ fires when the work is measurably done, not when the model says it is done.

2) turn cap
→ a hard ceiling on iterations, counted and enforced by the harness, not the prompt.
→ fires on the task it was never going to finish, before you pay to find that out.

3) budget cap
→ a limit on tokens or dollars, whichever one runs out first.
→ fires mid-run, which is exactly why it is the exit that saves you the 3am bill.

4) wall clock
→ a deadline on elapsed time, independent of how much progress was made.
→ fires when the run collides with a deploy window or the start of business hours.

5) no progress
→ hash the state every turn and compare it against the last few.
→ fires when three turns in a row change nothing. busy is not the same as moving.

6) human interrupt
→ an approval gate before risky steps, plus a kill switch that lives outside the loop.
→ fires whenever you decide, and it is the one exit the model cannot argue with.

7) error threshold
→ a counter of consecutive failures that resets on any success.
→ fires at n in a row, so it halts instead of retrying into the same wall all night.

8) external event
→ a webhook or a poll on whatever the task was actually about.
→ fires when the PR merged or the ticket closed and the work stopped mattering.

a loop with one exit hangs. a loop with eight is a system.

write the exits before you write the prompt.

[Sprytix @Sprytixl]

https://t.co/YgrHCOwDs0