Promix

I notice everything when others are still asleeping

[Promix @Promixyz]

I found an Anthropic repo worth saving if your coding agent keeps losing the plot between sessions.

It includes TWO prompts: one to set up the project, another to pick up the work.

The handoff is the interesting part ↓

01 / The implementation

An autonomous-coding demo built around an initializer and a coding agent.

https://t.co/KQjlUjBHxv

02 / The first-session prompt

Inspect how it sets up the feature checklist, environment, and project history.

initializer_prompt.md

03 / The continuation prompt

Read the existing state. Check the app. Work on one feature. Leave a useful handoff.

coding_prompt.md

04 / The engineering explanation

Why a large prompt and repeated “keep going” messages aren’t enough.

https://t.co/ce08MuHI3r

My suggested test:

Pause after a small, verified change. Restart with fresh context.

Can the next session identify what passed, what failed, and what to do next without you retelling the project?

That’s the handoff worth testing.

Start with a small feature list and a bounded run.

Save the video + prompts for your next multi-session build ↓

[Promix @Promixyz]

https://t.co/BubPwifse2

[Promix @Promixyz]

the most expensive AI mistake might be paying a genius to use Ctrl+F

XERJ Reader is built for a surprisingly simple job

Find the answer in the source document, or admit it isn’t there

Contract clauses, codebase searches, missing information

No creative writing contest. No 800-word philosophical explanation.

just give me the damn evidence

[Ivan Novikov @d0znpp]

I've built a new AI model that is not allowed to generate text. Instead, it produces a verifiable program that answers your prompt using the original reference.

XERJ Reader-1 is a neurosymbolic model for working with data. It points to the exact words in your document, writes a small program over them, and exact code computes the answer:

• 20-90x faster
• 10-1,000x cheaper (output tokens free)
• every answer is a program you can replay

The shortest path to automating data work.

[Promix @Promixyz]

I found an MCP pattern worth saving: Anthropic reports an example going from 150,000 tokens to 2,000.

The trick is where the work happens.

Load tool definitions on demand. Process intermediate data in code. Return only what the model needs.

That’s the architecture in the video ↓

01 / Read the engineering breakdown

The reported reduction is 98.7% in that example—not a promise of 98.7% off your entire agent bill.

https://t.co/QFr4OaeKp2

02 / Build the MCP connection

Official TypeScript SDK for MCP clients and servers.

https://t.co/99el4ygk3J

03 / Explore Code Mode

Cloudflare’s experimental implementation lets the model write code that calls tools.

https://t.co/CYbxmVnCWI

04 / Inspect a working pattern

This example wraps an MCP server behind a single code tool.

https://t.co/Sv8HLeutUf

My suggested experiment:

Pick one workflow that returns too much data.

Filter it in code before returning it to the model.

Compare task success, token usage, latency, and execution cost.

Keep execution sandboxed, permissioned, and resource-limited.

Save the architecture + repos for your next agent build ↓

[Promix @Promixyz]

https://t.co/BubPwifse2

[Promix @Promixyz]

Claude Code really spent $107 and 27 minutes building an Arctic submarine that looks like it was designed by someone who’s never seen the ocean 😭

Meanwhile Fable 5.2 built an entire cinematic Arctic expedition with a detailed submarine, floating ice and golden sunset reflections for just $8 in 3 minutes

Same prompt, 4 AI workflows

The $99 reality check 💀

🥇 Fable 5.2 → $8 / 3 min
🥈 GPT-6 Astra → $32 / 8 min
🥉 Kimi K3.1 → $63 / 18 min
💀 Claude Code → $107 / 27 min

That’s 13.4x the cost and 9x the time for Claude Code in this comparison

But here’s what actually caught my attention

Fable didn’t just make a better-looking submarine

It built an entire atmosphere around it, with convincing ice formations, reflections and lighting that make the scene feel like a movie

And Claude Code?

$107 to prove that even submarines can have a midlife crisis 😭

[Promix @Promixyz]

Claude Code really spent $34 and 28 minutes building a steam locomotive that looks like a Minecraft funeral train with a giant egg on top 😭

Meanwhile Kimi K3.1 built an entire cinematic railway scene with a detailed steam engine, massive smoke clouds and a stone viaduct for just $4 in 3 minutes

Same prompt, 4 AI workflows

The railway disaster report 💀

🥇 Kimi K3.1 → $4 / 3 min
🥈 GPT-6 Astra → $13 / 11 min
🥉 Blender MCP → $22 / 20 min
💀 Claude Code → $34 / 28 min

Kimi was 8.5x cheaper and 9.3x faster than Claude Code in this comparison

And here’s where it gets embarrassing

GPT-6 Astra delivered a detailed locomotive crossing a stone bridge, while Blender MCP nailed the moody nighttime atmosphere

Meanwhile Claude Code apparently forgot what a train looks like and started building a Minecraft monument 😭

Imagine paying $34 for a steam locomotive and getting a giant egg on railway tracks 💀

[Promix @Promixyz]

i coded a Grok Bot demo with 5 agents, loaded a $100 paper balance and hit run.

20 trades later: $318.42.

but one token got no money at all. i'd coded a second agent to challenge every buy and it found something that killed the order.

here's what it caught ↓

dev-linked wallets held 28% of the supply.

GUARD rejected the candidate. no order went through.

i wanted that decision to be as visible as a winning trade.

so i split the terminal into 5 jobs:

SMART$ follows wallet activity.
SNIPER watches new launches.
ROUTER checks the swap route.
GUARD checks risk.
TRADER handles paper execution.

the important bit is the handoff.

finding a signal doesn't automatically authorize a buy.

the candidate has to get through a separate risk check before execution.

then i made the interface show the whole chain:

the token appears.
the agents process it.
the order enters the feed.
the balance and chart update.
the recap reconciles the session.

including the ugly parts.

14 wins.
6 losses.

best: BLOOM, +$36.40.
worst: WAVE, −$7.20.

the red trades stayed on screen.

the rejected candidate got a notification.

and the final numbers came from the same scripted ledger:

$100 starting balance.
+$218.42 net.
$318.42 at the close.
20 closed positions.
zero left open.

to be clear: i coded the interface and session replay. these are simulated trades, not a live Grok integration or real trading returns.

the result i'm keeping is the architecture.

before connecting an AI signal to execution, i'd want five things:

→ a separate risk check
→ explicit veto power
→ a reason recorded for each rejection
→ losses visible beside wins
→ a balance that reconciles against the fills

save that checklist before you build yours.

the question i want my terminal to answer isn't just “what did it buy?”

it's “what stopped it from buying?”

[Lummox @Lummox_eth]

https://t.co/lhccExgzBr

[Promix @Promixyz]

Source: https://t.co/gwF8NxMvPC

[Promix @Promixyz]

Your agent’s memory can become its distraction.

More history means more information to sort through not automatically better decisions.

Anthropic’s context engineering guide offers a useful blueprint:

01 / Retrieve just in time
Load relevant information when needed.
02 / Trim tool outputs
Keep useful results. Remove redundant payloads.

03 / Compact carefully
Preserve decisions, open problems, and essential implementation details.

04 / Write persistent notes
Keep project state outside the context window. Reload it when needed.

05 / Isolate exploration
Let focused subagents return concise summaries.

06 / Choose representative examples
Teach the pattern without listing every edge case.

My suggested test:

Same model.
Same tasks.
Different context strategy.

Compare completion quality, tool calls, latency, and cost.

Save the map for your next agent debugging session.

[Promix @Promixyz]

https://t.co/BubPwifse2

[Promix @Promixyz]

Your RAG pipeline can lose the answer before the LLM even sees it.

Anthropic reported 67% fewer retrieval failures using contextual retrieval + reranking.

Here’s the architecture behind that number:

01 / Give each chunk its identity back

“Revenue rose 8%.”

Which company? Which quarter?

Before indexing, prepend a short explanation that connects the chunk to its source document.

02 / Search two ways

Embeddings capture semantic similarity.
BM25 helps find exact terms and identifiers.
Use both. Combine the results. Remove duplicates.

03 / Rerank the evidence

Finding something similar isn’t the same as finding the best evidence.

In the experiment, 150 candidates were reranked down to 20.

The reported retrieval failure rates:

→ Baseline: 5.7%
→ Contextual embeddings + contextual BM25: 2.9%
→ Add reranking: 1.9%

That’s a 67% relative reduction in retrieval failures—not a 67-point jump in answer accuracy.
Before your next model upgrade, try this:
Same corpus.
Same labeled questions.
Three retrieval configurations.

Compare retrieval misses, answer support, latency, and cost.

Research:
https://t.co/I5jJQ89sSC

Implementation cookbook:
https://t.co/e0EchtPmpg
Save this architecture for your next RAG debugging session.

[Promix @Promixyz]

https://t.co/BubPwifse2

[Promix @Promixyz]

Claude Code really spent $34 and 28 minutes building a steam locomotive that looks like a Minecraft funeral train with a giant egg on top 😭

Meanwhile Kimi K3.1 built an entire cinematic railway scene with a detailed steam engine, massive smoke clouds and a stone viaduct for just $4 in 3 minutes

Same prompt, 4 AI workflows

The railway disaster report 💀

🥇 Kimi K3.1 → $4 / 3 min
🥈 GPT-6 Astra → $13 / 11 min
🥉 Blender MCP → $22 / 20 min
💀 Claude Code → $34 / 28 min

Kimi was 8.5x cheaper and 9.3x faster than Claude Code in this comparison

And here’s where it gets embarrassing

GPT-6 Astra delivered a detailed locomotive crossing a stone bridge, while Blender MCP nailed the moody nighttime atmosphere

Meanwhile Claude Code apparently forgot what a train looks like and started building a Minecraft monument 😭

Imagine paying $34 for a steam locomotive and getting a giant egg on railway tracks 💀

[Lummox @Lummox_eth]

https://t.co/vBZrzu28w5

[Promix @Promixyz]

GPT-6 Astra really spent $34 and 28 minutes building Venice like someone recreated it in Minecraft with their eyes closed 😭

Meanwhile Blender MCP made an entire cinematic Venetian canal with golden reflections, detailed architecture and a luxury gondola for just $4 in 3 minutes 💀

Same prompt, 4 AI workflows

The Venice disaster report 🛶

🥇 Blender MCP → $4 / 3 min
🥈 Opus 5.5 → $13 / 11 min
🥉 Kimi K3.1 → $22 / 20 min
💀 GPT-6 Astra → $34 / 28 min

Astra was 8.5x more expensive and 9.3x slower than Blender MCP in this comparison

But here's where it gets embarrassing

Blender MCP delivered warm streetlights, realistic water reflections and a gondola that actually looks luxurious

Meanwhile Astra gave us blue Minecraft water, concrete boxes and a boat that looks like it was carved out of a potato 😭

Imagine paying $34 for a romantic trip to Venice and getting the Roblox free trial instead 💀

[Lummox @Lummox_eth]

https://t.co/vBZrzu28w5

[Promix @Promixyz]

Claude 3.7 Sonnet spent $102 and 28 minutes creating what might be the most expensive chocolate donut in AI history 😭

Meanwhile GPT-6 Astra built a lava-covered volcano for $40, and Blender MCP turned the same prompt into a full Hollywood disaster scene for $67 💀

Same prompt, 4 AI workflows

Now look at what $102 gets you

🥇 Blender MCP → $67 / 20 min
🌋 GPT-6 Astra → $40 / 11 min
⚡ Kimi K3.1 → $12 / 3 min
🍩 Claude 3.7 Sonnet → $102 / 28 min

Kimi finished 9.3x faster than Claude, while Blender delivered the most dramatic eruption

Astra landed somewhere in between with detailed lava and a cleaner volcanic surface
And Claude?

28 minutes, $102, and a mountain that looks ready to be sold at Dunkin'

The most expensive model didn't win the race

It opened a bakery

[Lummox @Lummox_eth]

https://t.co/vBZrzu28w5

[Promix @Promixyz]

Claude Opus 5.5 really charged $48 to build a mechanical rhino that GPT-6 Astra made for $22 😭

Same 73,928 triangles, same 606 mechanical parts, same armored beast

And somehow Opus wants more than DOUBLE the money 💀

The damage report

🥇 GPT-6 Astra → $22
💀 Claude Opus 5.5 → $48
That's a 2.18x price difference for what looks like the exact same result

But the actual engineering challenge is pretty wild

⚙️ 606 mechanical parts
🦏 Telescoping drill horn
🔩 Hydraulic legs and peeling armor
⚡ 1,166 RPM core mechanism

The rhino extends its horn, opens its armor and charges into a target
And I'm supposed to believe the extra $26 bought us something other than a more expensive invoice 😭

[Lummox @Lummox_eth]

https://t.co/vBZrzu28w5

[Promix @Promixyz]

It's becoming f*cking ridiculous how Claude Code can turn a $5,000-looking electronics demo into an interactive Raspberry Pi lab, where boards, breadboards, and jumper wires assemble step by step while a live terminal tracks every connection

Forget spending weeks manually building another hardware animation

Imagine opening a browser and watching an entire electronics workstation assemble itself, from an empty circuit board to a fully wired Raspberry Pi setup

The video shows a step by step hardware walkthrough with component placement, jumper wire routing, startup checks, and a live diagnostic interface

But here's where things get interesting

Instead of animating every wire and component separately, you can create a reusable assembly engine that controls the entire sequence through code

And you can build the concept with Claude Code

Here's the prompt worth saving:

"Build a premium interactive Raspberry Pi electronics laboratory using React, Three.js, and TypeScript

Create detailed 3D models of a Raspberry Pi, breadboard, GPIO pins, jumper wires, and electronic components

Animate a complete hardware setup from an empty workspace to a fully connected circuit

Use procedural cable curves that connect to exact pin coordinates

Add interactive assembly stages, component inspection, wiring validation, and simulated terminal diagnostics

Build a live dashboard showing GPIO connections, assembly progress, hardware status, and execution logs

Use a clean editorial interface, smooth camera transitions, and realistic component materials

Keep hardware configuration separate from rendering so different circuit projects can reuse the same engine"

The clever part isn't the Raspberry Pi itself

It's that the same system could visualize Arduino projects, IoT devices, robotics controllers, and entire electronics tutorials

One reusable engine instead of hundreds of manually animated scenes

Bookmark this prompt before your next Claude Code project

[Promix @Promixyz]

https://t.co/QeXi97WMxr

[Promix @Promixyz]

Claude Opus 5.5 really needed 56 seconds to build a mechanical hornet that GPT-6 Astra finished in just 18 😭

Same prompt, same robotic insect, almost identical results

And somehow Astra is 3.1x faster 💀

The damage report

🥇 GPT-6 Astra → 18s
💀 Claude Opus 5.5 → 56s
Both had to build a mechanical hornet with articulated legs, moving wings, antennae and an animated stinger

But here's the funny part

The final models look so similar that you'd struggle to tell which AI made which one

So what exactly are those extra 38 seconds buying you? 😭

We're reaching a point where the real AI competition isn't just about making something work

It's about how fast you can get there

[Promix @Promixyz]

https://t.co/QeXi97WMxr

[Promix @Promixyz]

Claude Code might have just lost a 3D competition to the software it's supposed to control

$16.50 vs $9.50

Same mechanical wolf, same animation, almost half the cost

And here's where it gets interesting 💀

🐺 52 armor plates
⚙️ 28 leg actuators
🦷 10 mechanical fangs
🔩 8 articulated tail links
🔋 80 RPM internal mechanism

Both workflows handle the same 3D assembly with moving joints, head tracking and jaw animation

But Blender's displayed cost is 42% lower

The real challenge isn't generating 65,248 triangles

It's keeping hundreds of mechanical parts synchronized when the wolf starts moving

imagine spending more on AI just to watch Blender build the same robot for less 😭

[Promix @Promixyz]

https://t.co/QeXi97WMxr

[Promix @Promixyz]

Claude Code + Blender is becoming f*cking illegal

You can literally build a fully animated jet engine with 18 fan blades, 6 compressor stages and a spinning turbine without manually modeling every single component

And the entire engine actually moves

The workflow is surprisingly simple

→ Claude Code generates the engine geometry and internal structure
→ Blender handles the rotating shaft, materials and animation
→ 48 turbine blades and 8 nozzle petals complete the assembly
→ The cutaway view exposes the entire mechanism in motion

The crazy part isn't just making a jet engine look realistic

It's coordinating dozens of moving components, keeping everything aligned and animating the core at 8,700 RPM

bro imagine spending days modeling a turbine just to watch someone describe the whole mechanism in a prompt

[Lummox @Lummox_eth]

https://t.co/vBZrzu28w5

[Promix @Promixyz]

Same prompt. Completely different Three.js planets.

I asked GPT and Claude for one HTML file: a tiny planet, a city, spin, day→night, sound.

GPT shipped 5 modules : ocean, coastal city, sun orbit, aurora, soft pad. Warm windows that actually light up at night.

Claude also shipped 5 : dark core, 180 neon towers, orbital rings, holograms, data rain. Same brief. Opposite taste.

Identical prompt. Divergent craft.

[Promix @Promixyz]

https://t.co/QeXi97WMxr

[Promix @Promixyz]

AI agents are getting f*cking illegal 😭

A year ago, building an AI system meant stitching together APIs, writing endless glue code and praying nothing broke when one agent handed work to another

Now open-source tools are turning that entire mess into infrastructure you can actually build, inspect and scale

8 GitHub repos worth bookmarking ↓

1. LangGraph
→ Stateful AI agents and orchestration
↳ https://t.co/47iijL7M57

2. CrewAI
→ Multi-agent teams and task delegation
↳ https://t.co/67UxuGietJ

3. Temporal
→ Durable workflows, retries and recovery
↳ https://t.co/aHmSaNqvcm

4. Dapr
→ Communication between microservices
↳ https://t.co/PrHfcgt9p8

5. NATS
→ Fast messaging between distributed services
↳ https://t.co/U4fzvi8oyc

6. Langfuse
→ AI tracing, debugging and observability
↳ https://t.co/hTgXhmn1Ul

7. OpenTelemetry
→ Distributed traces, logs and metrics
↳ https://t.co/5frJHATKe3

8. K3s
→ Lightweight Kubernetes for deployment
↳ https://t.co/EibKIcRRwa

The crazy part isn't having 128 services

It's being able to understand how they connect, where execution breaks and which component needs fixing

bookmark this before building your next AI system

[Promix @Promixyz]

https://t.co/QeXi97WMxr

[Promix @Promixyz]

Elon Musk's Grok is more than an AI chatbot

Most people use Grok to ask questions, but xAI gives developers something far more interesting

A way to build AI systems that reason, search the web, analyze information, and use tools

Here's the 8-step roadmap to building with Grok 👇

1 → Get your xAI API key at https://t.co/xoN0kmc0d9

2 → Install the OpenAI Python SDK with pip install openai

3 → Connect to https://t.co/gfwlClBxbh

4 → Choose a Grok model for your task

5 → Enable web search when you need fresh information

6 → Add function calling to connect your own tools

7 → Test outputs, verify sources, and handle failures

8 → Deploy a real application and monitor its performance

The interesting part isn't asking Grok better questions

It's connecting reasoning, live information, and real tools into one working system

Full documentation: https://t.co/JQmX6rNl1n

I mapped the workflow into one visual guide

Save this before building your first Grok-powered application

[Lummox @Lummox_eth]

https://t.co/lhccExgzBr

[Promix @Promixyz]

5 GitHub repos can turn Claude Code into an entire AI engineering team

Not just one AI writing code, but parallel agents building features, reviewing changes, running tests, and coordinating work without destroying each other’s progress

Here’s how to build it 👇

(1/5) Claude Code
run specialized coding agents for independent development tasks
▸ https://t.co/SkjKYCjDnK

(2/5) LangGraph
coordinate parallel workflows, track dependencies, and manage execution state
▸ https://t.co/47iijL7M57

(3/5) BullMQ
queue jobs, control concurrency, and automatically retry failed tasks
▸ https://t.co/hs97wxeE0E

(4/5) Git
isolate agents in separate worktrees so they don’t overwrite each other’s code
▸ https://t.co/60jd2JJ1vM

(5/5) Playwright
test completed features in a real browser before merging
▸ https://t.co/6leZ4QgPiQ

The interesting part?

You don’t need dozens of agents to start

Give Claude Code this workflow:

PLAN: break the project into independent tasks with clear acceptance criteria

ISOLATE: create a separate Git worktree for each coding worker

EXECUTE: assign tasks to parallel agents with explicit file ownership

VERIFY: run tests, review changes, and merge only validated work

The catch?

More agents don’t automatically mean faster development

Without proper coordination, they’ll duplicate work, overwrite files, and introduce conflicting changes

Start with 4 workers, not 80

Copy this prompt into Claude Code:

“Analyze my repository and design a 4 agent development workflow, split the project into independent tasks, identify dependencies, assign file ownership, create isolated Git worktrees, and define automated tests and merge conditions for every task”

One agent plans

Four workers execute

Tests decide what gets merged

Save these 5 repos before building your own AI engineering team

[Promix @Promixyz]

https://t.co/QeXi97WMxr