Get live statistics and analysis of amnox's profile on X / Twitter

Software Engineer | LLMs loooooove @shedboxai

682 following240 followers

The Innovator

amnox is a software engineer deeply passionate about Large Language Models and AI-driven tools, constantly exploring how these technologies can enhance life and productivity. Their tweets reveal a blend of sharp tech insights and a playful, entrepreneurial spirit that loves to challenge conventional approaches. They balance technical depth with a genuine curiosity about building meaningful, user-centered AI applications.

Impressions
16.8k-13.4k
$3.15
Likes
20-12
63%
Retweets
1
3%
Replies
6-4
19%
Bookmarks
5-1
16%

amnox’s ā€˜real founder’ badge must come with a lifetime subscription to ā€˜429 Too Many Requests’—because who needs sleep when you’re drowning in API calls and existential tech crises?

Building and promoting a no-code data processing framework that integrates seamlessly with LLMs, demonstrating a concrete contribution to making advanced AI tech more accessible and powerful.

To pioneer innovative AI solutions that simplify complex problems and enhance human potential by leveraging Large Language Models in practical, intuitive ways.

amnox values creativity in technology, the power of AI to transform everyday life, and the importance of building tools that genuinely understand and assist users. They believe in transparency about challenges and setbacks, using them as learning opportunities to iterate smarter.

Their strengths lie in innovative thinking, practical AI application, and communicating complex ideas with humor and clarity. They excel at spotting new trends and gaps in AI tech and aren’t afraid to critique or pivot based on community feedback.

A potential weakness could be spreading themselves too thin, as indicated by their hesitation to juggle multiple projects or the reliance on platforms and models like Claude that are subject to volatile public opinion, which might affect their audience engagement.

To grow their audience on X, amnox should leverage their witty insights and deep niche expertise by consistently sharing unique AI project updates, technical challenges, and solutions. Engaging in active dialogues with AI enthusiasts and positioning themselves as a go-to for practical AI engineering advice could boost follower trust and interactive growth.

Fun fact: amnox jokes that real founders have error logs full of '429 Too Many Requests,' highlighting their hands-on, deeply technical approach to API-heavy projects and their immersion in the realities of software engineering.

Top tweets of amnox

The tools that completely changed my AI coding game: šŸ› ļø @shedboxai - workflow orchestration šŸ› ļø Claude Code - complex reasoning šŸ› ļø Linear - task breakdown šŸ› ļø Obsidian - knowledge management šŸ› ļø Terminal - because CLI > GUI always Your essential stack? šŸ‘‡ #DevStack #AItools #Productivity #ToolTalk

48

Ever wondered how an ā€œagentā€ works? Here’s an example describing what each component is responsible for: Incoming request: ā€œBook me a flight from NYC to London for next Friday under $500ā€ 1. Orchestrator receives request •Starts the control loop •Initializes empty state 2. Orchestrator → Memory ā€¢ā€œDo we have any context about this user?ā€ •Memory returns: ā€œUser prefers aisle seats, has payment info savedā€ 3. Orchestrator → Planner ā€¢ā€œHere’s the goal: book flight. Here’s context from memory. Make a plan.ā€ •Planner returns: [ā€œSearch available flightsā€, ā€œFilter by budgetā€, ā€œBook cheapest optionā€] 4. Orchestrator → LLM ā€¢ā€œExecute step 1: Search available flights. Here’s user request + context + plan.ā€ •LLM returns: ā€œI need to call search_flights(origin=ā€˜JFK’, dest=ā€˜LHR’, date=ā€˜Oct 17’)ā€ 5. Orchestrator → Tool Executor ā€¢ā€œExecute search_flights with these paramsā€ •Tool returns: JSON with 3 flight options ($450, $520, $630) 6. Orchestrator → Memory ā€¢ā€œSave these flight resultsā€ •Memory stores them 7. Orchestrator → LLM (next iteration) ā€¢ā€œStep 2: Filter by budget. Here’s what we found.ā€ •LLM returns: ā€œOnly the $450 flight fits. I’ll book it. Call book_flight(id=ā€˜FL123’, seat_pref=ā€˜aisle’)ā€ 8. Orchestrator → Tool Executor ā€¢ā€œExecute book_flightā€ •Tool returns: ā€œSuccess! Confirmation #ABC789ā€ 9. Orchestrator → Memory ā€¢ā€œSave booking confirmationā€ 10. Orchestrator checks termination •Goal achieved? Yes, flight booked. •Stops the loop. 11. Orchestrator returns final response ā€¢ā€œBooked! Your flight is confirmed, confirmation #ABC789ā€ Each component has ONE job. Orchestrator just passes messages between them.

102

Damn @claudeai I wasn't familiar with your game

13

Most engaged tweets of amnox

Ever wondered how an ā€œagentā€ works? Here’s an example describing what each component is responsible for: Incoming request: ā€œBook me a flight from NYC to London for next Friday under $500ā€ 1. Orchestrator receives request •Starts the control loop •Initializes empty state 2. Orchestrator → Memory ā€¢ā€œDo we have any context about this user?ā€ •Memory returns: ā€œUser prefers aisle seats, has payment info savedā€ 3. Orchestrator → Planner ā€¢ā€œHere’s the goal: book flight. Here’s context from memory. Make a plan.ā€ •Planner returns: [ā€œSearch available flightsā€, ā€œFilter by budgetā€, ā€œBook cheapest optionā€] 4. Orchestrator → LLM ā€¢ā€œExecute step 1: Search available flights. Here’s user request + context + plan.ā€ •LLM returns: ā€œI need to call search_flights(origin=ā€˜JFK’, dest=ā€˜LHR’, date=ā€˜Oct 17’)ā€ 5. Orchestrator → Tool Executor ā€¢ā€œExecute search_flights with these paramsā€ •Tool returns: JSON with 3 flight options ($450, $520, $630) 6. Orchestrator → Memory ā€¢ā€œSave these flight resultsā€ •Memory stores them 7. Orchestrator → LLM (next iteration) ā€¢ā€œStep 2: Filter by budget. Here’s what we found.ā€ •LLM returns: ā€œOnly the $450 flight fits. I’ll book it. Call book_flight(id=ā€˜FL123’, seat_pref=ā€˜aisle’)ā€ 8. Orchestrator → Tool Executor ā€¢ā€œExecute book_flightā€ •Tool returns: ā€œSuccess! Confirmation #ABC789ā€ 9. Orchestrator → Memory ā€¢ā€œSave booking confirmationā€ 10. Orchestrator checks termination •Goal achieved? Yes, flight booked. •Stops the loop. 11. Orchestrator returns final response ā€¢ā€œBooked! Your flight is confirmed, confirmation #ABC789ā€ Each component has ONE job. Orchestrator just passes messages between them.

102

The tools that completely changed my AI coding game: šŸ› ļø @shedboxai - workflow orchestration šŸ› ļø Claude Code - complex reasoning šŸ› ļø Linear - task breakdown šŸ› ļø Obsidian - knowledge management šŸ› ļø Terminal - because CLI > GUI always Your essential stack? šŸ‘‡ #DevStack #AItools #Productivity #ToolTalk

48

Damn @claudeai I wasn't familiar with your game

13

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