Anshuman Tiwari

Building @JabrodOfficial

[Anshuman Tiwari @Realthugus]

Well agentbill now got listed: https://t.co/5s4vtYw3EA

https://t.co/X8JLYYHKeo

well @prasad_pilla any thoughts on it?

[Anshuman Tiwari @Realthugus]

new touch screen macbook coming today.

[Jabrod @JabRodOfficial]

Shorter embeddings can reduce vector storage, but retrieval quality still needs testing. For models that support Matryoshka truncation, compare supported dimensions on the same labelled queries before choosing the smallest vector.

[Unnati @Unnati_builds24]

Whenever I feel demotivated i rewatch this...

Elon Musk in a living room in 2008, 12 days from bankruptcy, pitching a $109,000 car to people...

[Anshuman Tiwari @Realthugus]

alan code agents built 5 parts of agentbill.
then agentbill priced those runs: 128 calls, $0.62.
then it went back into alan as a budget skill.
weird loop. i like it
@tryAlanAI @prasad_pilla #OpenFire

[Anshuman Tiwari @Realthugus]

IBM COOKED

[How To Prompt @HowToPrompt__]

RAG is officially cooked.

IBM built a retriever that hallucinates 65x less than fine-tuned RAG systems.

Right now, every company is using Retrieval-Augmented Generation (RAG) to stop their AI from hallucinating.

The idea is to give the AI a private database to read so it doesn't make things up.

But there is a massive structural flaw.

Standard RAG blindly grabs documents based on vector similarity and forces the AI to read them. If the retriever pulls irrelevant garbage, the AI doesn't stop.

It just hallucinates with citations.

IBM just dropped a paper that changes the math entirely.

They built a new retrieval architecture that hallucinates 65x less than state-of-the-art fine-tuned RAG systems.

65x is the difference between a fun prototype and a production-grade enterprise system.

Instead of blindly dumping context into the model and hoping for the best, their system actively filters and validates the retrieved data before the generation step even begins.

It cuts out the noise. It forces absolute precision.

For businesses, this is the holy grail.

Zero hallucinations means zero liability. It means autonomous agents can finally be trusted with sensitive company data without a human babysitting every output.

[How To Prompt @HowToPrompt__]

RAG is officially cooked.

IBM built a retriever that hallucinates 65x less than fine-tuned RAG systems.

Right now, every company is using Retrieval-Augmented Generation (RAG) to stop their AI from hallucinating.

The idea is to give the AI a private database to read so it doesn't make things up.

But there is a massive structural flaw.

Standard RAG blindly grabs documents based on vector similarity and forces the AI to read them. If the retriever pulls irrelevant garbage, the AI doesn't stop.

It just hallucinates with citations.

IBM just dropped a paper that changes the math entirely.

They built a new retrieval architecture that hallucinates 65x less than state-of-the-art fine-tuned RAG systems.

65x is the difference between a fun prototype and a production-grade enterprise system.

Instead of blindly dumping context into the model and hoping for the best, their system actively filters and validates the retrieved data before the generation step even begins.

It cuts out the noise. It forces absolute precision.

For businesses, this is the holy grail.

Zero hallucinations means zero liability. It means autonomous agents can finally be trusted with sensitive company data without a human babysitting every output.

[Anshuman Tiwari @Realthugus]

@prasad_pilla well if found useful can integrate into alan itself as a skill?

[Anshuman Tiwari @Realthugus]

try it in 3 lines:
git clone https://t.co/5mWbOQ4a8e
npm i && npm run build && npm link
agentbill

or just poke the live demo: https://t.co/S5zrRmvaFj

[Anshuman Tiwari @Realthugus]

try it in 3 lines:
git clone https://t.co/5mWbOQ4a8e
npm i && npm run build && npm link
agentbill

or just poke the live demo: https://t.co/S5zrRmvaFj

[Anshuman Tiwari @Realthugus]

what agentbill actually does for u:
- prices every model call from logs ur agents already write
- shows which file reads and cache rebuilds cost the most
- tells u what an open model would charge for the same tokens
no proxy, no account. it just reads files
#OpenFire

[Anshuman Tiwari @Realthugus]

what agentbill actually does for u:
- prices every model call from logs ur agents already write
- shows which file reads and cache rebuilds cost the most
- tells u what an open model would charge for the same tokens
no proxy, no account. it just reads files
#OpenFire

[Anshuman Tiwari @Realthugus]

submitted agentbill for openfire.
24 hours, one laptop, a bill for every agent session.
if u use claude code or codex, run it once on ur own logs. u wont like the number
@tryAlanAI @prasad_pilla
https://t.co/bdnpvT7Ko0

[Anshuman Tiwari @Realthugus]

submitted agentbill for openfire.
24 hours, one laptop, a bill for every agent session.
if u use claude code or codex, run it once on ur own logs. u wont like the number
@tryAlanAI @prasad_pilla
https://t.co/bdnpvT7Ko0

[Anshuman Tiwari @Realthugus]

built agentbill for #OpenFire @tryAlanAI @prasad_pilla

problem: coding agents bill per call, nobody sees where it goes. mine: $4.8k, 23% leaked

it reads agent logs, prices each call, shows the leaks

built w alan code agents on deepseek: 5 tickets, $0.62, priced by agentbill

[Tanay Kothari @tankots]

most ai products right now are built for the 5% of people who are tech savvy.

my dad is never going to write a system prompt. my grandfather doesn't even know what a prompt is.

that means it's the best time ever to use ai and build products for the other 95%.

[Anshuman Tiwari @Realthugus]

Deepdown each conversational LLM is a human labourer iykyk.

[Anshuman Tiwari @Realthugus]

checkout here: https://t.co/5mWbOQ4a8e

[Anshuman Tiwari @Realthugus]

$4800 that's what I spent in months of building across agents sessions.

[Anshuman Tiwari @Realthugus]

checkout here: https://t.co/5mWbOQ4a8e

[Anshuman Tiwari @Realthugus]

$4800 that's what I spent in months of building across agents sessions.

[Anshuman Tiwari @Realthugus]

built agentbill for #OpenFire @tryAlanAI @prasad_pilla

problem: coding agents bill per call, nobody sees where it goes. mine: $4.8k, 23% leaked

it reads agent logs, prices each call, shows the leaks

built w alan code agents on deepseek: 5 tickets, $0.62, priced by agentbill