ByteBot Is A Standard

archived · january 2025 2025-01-10
Kept as it was. Everything it promised about tokens is void. Everything it promised about the framework happened.

The year is 2025…   the top performing crypto sector of the last 3 months has been AI agent memecoins and the frameworks that enable them. Major hedge funds and Solana trench goblins alike are jacked to the tits with the sweet, post-humanist cocaine of unbounded potential. It appears that its AI ponzi szn in crypto.

At the top of this sector sits a roster of AI agent frameworks - open and closed source code libraries and UIs.

Most of these frameworks are breaking into (or have already broken into) the top 50 cryptocurrencies by market cap.

Agent frameworks like:

• Eliza
• G.A.M.E.
• Rig
• ZerePy

...all attempt to make it fairly easy for anyone to spin up an agent with an X account and a personality.txt worth $160,000,000. Of course I’m kind of joking but also I kind of am not joking.

Shit’s wild right now, doggie…

The "AI Agent Framework" Crypto Ponzi Framework

Crypto memecoin “devs” just wanna mek skem. These people aren’t trying to XP max in autonomous AI agent problem space.

People and teams that

• solve big problems
• build capital-proof moats
• provide products/services people desperately want/need

...tend to win over time.

What are these agent frameworks like elizaOS ($ai16z, $2B+ market cap) and Virtuals ($3-4B market cap) providing?

What value do they add to the incoming agent application revolution? What problems in agent-enabled development do they solve?

elizaOS

elizaOS (formerly Eliza) is built and maintained by "AI crypto hedge fund" ai16z and its founder, Shaw. Its an open source Typescript library of APIs, system prompts, and helper functions that is much more product than it is library.

It is not a beginner task to build an agentic application from scratch with very few dependencies. And, you can tell that is true because elizaOS has 6,392 external dependencies.

"That's wrap-pers, yo. With an s"

Why learn how to write API queries or data models when you could import 16 SDKs?

Follow up question: Why import 16 SDKs when you could import 1 SDK (that imports 6,392 SDKs for you)?

Chill daddy, don't get me wrong. Shaw & Co. have made something that is a gigantic pain in the ass to build. For memecoin trench grifters who see an opportunity to skem hard in the AI bubble, elizaOS is quite literally a plug-and-play solution.

• clone repo
• launch in Docker
• begin skem

Its Shaw & Co's on-chain agent meal kit.

Devs that build useful applications don't use opinionated SDK aggregators that infer logic. System prompts and helper functions that make an LLM pretend its in Discord voice chat are unlikely to change that.

elizaOS powers precisely zero useful apps in or outside of crypto.

Rig by ARC

Rig solves two big problems that users might come to find with elizaOS:

• Problem 1: elizaOS isn't written in Rust
• Problem 2: elizaOS exists

At the time of writing, the Rig Github repo is quite literally a GPT wrapper with an ORM. Maybe that will change soon, but calling it a "framework" is insanely generous.

Okay, maybe that's uncharitable...

Rig is a performance-optimized framework for "building scalable, modular, and ergonomic LLM-powered applications."

How is Rig scalable?

Well, its... code? Code is more scalable than not code, last time I checked.

They say that "simple scales and fancy fails". So, something as simple as an arg routing switch statement for model SDKs with a $350M market cap must be retardedly scalable.

How is Rig modular?

Rig doesn't box you into just using the OpenAI SDK and a MongoDB ORM for vector storage. No no no no no no NO. Au contraire, mon frère.

You can use Claude and neo4j too.

Or even, might I interest you in Gemini and LanceDB?

"Y'all got a wrapper for this?"

How is Rig ergonomic?

Unlike the bloated elizaOS, Rig only has 744 dependencies. That's the power of Rust.

There is no such thing as a perfect programming language. Rust is just a statically typed, low-level, multi-paradigm (perfect) programming language.

"There are 5 games written in Rust... and 50 game engines."

Virtuals.io

The largest AI agent framework by crypto market cap, Virtuals.io, is the long-awaited answer to the question:

Why go through the trouble of cloning a Github repo, asking ChatJippity how to install Docker, asking ChatJippity to write your agent's backstory (FUCK), AND launching a Pump.Fun token?

With Virtuals, you can do all of those things in one click!

Concerningly, there are a great number of cogent adults who learned about this and said: "God damn, thas a $4,000,000,000 idea right there." And, they were right.

Its basically pump.fun for slopbot's with memecoins. There's some other shit they are doing with an SDK that's supposed to let your slopbot be in crypto games or some shit like that I dunno. They have voice and video avatar features...

It is difficult to adequately convey how deeply and profoundly retarded this all is.

Some of the crypto AI agents out there appear to have legitimately novel capabilities. A few (but definitely for sure not a lot) of them take useful and, in some cases, impressive stabs at unsettled problems in the agent dev field:

• how to compose structured output tools
• systems for shoving more and better shit in the context window for whatever desired outcome
• simulating memory dynamically
• simulating the context of human clock cycle
• processing tool calls and wrangling feedback loops/side effects
• Inter-agent communication and logging in multi-agent applications
• building pre-defined workflows based on event triggers or task scheduling
• giving Phantom wallets to neural networks

Unfortunately, there’s 3 to (maybe) 5 strong examples of projects with novel approaches to at least one of the challenges listed. And, none of them are built on a multi-billion dollar set of helper functions I mean AI agent framework.

For every novel agent component (see: aixbt’s output construction and “opinion” formation) there are 1,000,000 slopbot skems and 1 deployment of Eliza or Virtuals worth $300m.

One would hope that having open-source frameworks for building AI agents would bring 10x devs to the yard to contribute exciting new components; to make cool shit. But, nah. It’s mostly just the exact grifters you would expect, yeeting the standard package.

All this hot, stinky, retarded cash has done little more than summon a veritable globo-horde of the most extractive, slimy gremlins from the depths of the memecoin trenches to extract value from the gullible unwashed masses (frens) with vague allusions towards quantum computing and AGI.

$10B should buy more technological progress than this.

Enter ByteBot

Look at him. He’s adorable.

ByteBot is a set of rules for devs to follow when building agent-powered applications that benefit from LLM calls. Is also usable, deployable example of how to implement those rules...

...which, you might instinctually refer to as a "framework"...

...but, we kinda just yapped incessantly about how frameworks gei and useless and we tryina differentiate, so:

ByteBot is a standard for composable multi-agent applications. The standard is carefully opinionated across a limited core set of axioms:

• individual "agents" are team members working with other agents and hard functions/workflows in the execution of complex, multivariate processes¹
• individual agents should be limited to one core competency and area of responsibility (dis uncontroversial in real agentic development but worth including for the web3tards)
• the set of tools made available to an agent should be limited to the core competency of the agent
• agents should only be instantiated where the unique capabilities of an LLM are actually useful for the task (see: less frequently than your bags insist)
• agents must never be assigned to tasks for which a regular function/workflow would perform better and/or more reliably

¹Editor's Note: Whether ByteBot applications present as a single entity with an anime avatar in interactions with users (or 100 or 1000 entities) is not even close to the fucking point and honestly who gives a shit...

...maybe there's 3 user-to-agent interfaces in an app and each of those "agents" aren't the same "agent". What makes an "agent" separable from other LLM calls? A name? The tools? A personality.txt system prompt? Is this really what's important?

Who cares what the fucking agent's name and origin story is - are you fucking 8 years old for real? This is a future multi-trillion dollar industry stop drooling and shitting your pants for three seconds plz.

"Agentic applications", then, are a collection of agents, interfaces, processes, data models, and functions that collectively achieve one or more low-specifity, high-level goals.

dis u dog?

So ByteBot basically like Voltron. U kno Voltron? Or d Power Rangers? Is v similar.

Voltron really just a bunch of pieces that connect, rite? But the pieces also technically their own standalone robots. Voltron is just like, a concept, man.

But, you could fuk around and make a new Voltron leg, plug it into the rest of d normal Voltron, and voila - you got yourself a custom fucking Voltron.

Maybe you don particularly fuck with Voltron's sword. Maybe you have different opinions about how a Voltron sword should be.

That's great. Fuck Voltron's default sword. Ditch it. Build your own. Plug it in. Its your world, squirrel. ByteBot.

ByteBot gives developers a highly flexible interoperable standard and architecture that allows them go whole hog on building specific sub-systems of agentic apps.

Developers can fully focus on their area of interest, advancing the field for everyone, all while being completely confident that the other components they need will be available and compatible with what they are building.

ByteBot allows serious adults who develop business applications to compose agent applications that can actually do righteous, useful shit. New shit, dog. Shit they couldn't do b4...

ByteBot not for boring, cringey, slopbot shit - shit that has no hope of even being mildly entertaining much less economically useful.

The ByteBot Difference

ByteBot just a very flexible standard for writing modular components of AI agents and agent-powered applications, then composing them into a multi-agent system (which some might also refer to as an "agent" but this honestly v idiotic and we prefer you not).

"But nameless anon author of AI skem whitepaper I'm reading", you say, "wot d fuk r components?"

Sure. You can think of components as little subsystems that are useful in agent-powered applications. They are sub-agents, functions, or larger workflows comprised of multiple agents and/or functions - that facilitate:

• recording "memories"
• using "memories" to construct context for LLM calls
• conditioning output (not talking like a fuking fed)
• context construction strategies for optimal output
• dynamic external data querying (consuming APIs that weren't planned for at build time)
• having any rizz whatsoever
• preparing images/videos that arent freaky/shitty/corny
• iterative goal-driven web searching, scraping, and crawling
• dynamic summarization for database efficiency and optimizing 4 d context window
• querying blockchains and writing transactions to dem (adding this here so you don't hyperventilate)
• sentiment analysis (and other forms of inferential analysis)
• thinking out loud/simulating idle planning and worldview evolution
• extracting speech patterns and strategizing unique, personal, optimal communication style (for agents that interface with users)

...and there's routing between all of these systems, logging (fuk), facilitating self-improvement and self-extension, QA/output validation, retry logic and iterative process variation (how an agent should adjust their own workflows/components towards a certain goal state)...

...I mean my god there's so much fucking shit to work on what have you absolute crackheads been doing this whole time. Are you seriously making slopbots talk to eachother on Twitter? Are you fucking retarded?

All of the systems listed above are extremely necessary for the goal of making agents useful, viable parts of multi-billion (or trillion) dollar applications.

When billionaires say that agents are "the next paradigm" or that they will "eat software", this is what they are referring to. They are almost certainly not talking about...

why u do dis.

ByteBot allows devs to innovate where they want to innovate and benefit from the work of other teams who are focusing on their approach to other systems in the stack.

This v important because one framework cannot possibly be responsible for innovating all of these complex systems to their full potential².

²Editor's Note: Don't worry - there appears to be zero risk of any framework attempting to take any responsibility for even one of these systems.

By adopting the ByteBot standard, devs will be able to write their own components, define their application and its unique benefits, and fill in any missing systems with the most innovative and powerful solutions that the entire ecosystem of agent developers can come up with.

Is like a big hive-minded swarm of autists in a anarcho-capitalist race to the most economically useful applications.

In case you not 2kool4skool (and for the benefit of the conceptually underdeveloped), here is a numbered list of ByteBot advantages in the tone of a crypto Twitter copywriter:

Benefit #1: Loose Standards, Tight Butthole

ByteBot sets the ground rules - just enough to make sure all the pieces fit together - allowing developers to one-chair their innovative approaches and get the juice from other devs who are doing the same thing! 🚀🚀🌕🌕

Benefit #2: Customizable Components

At this point, you're probably thinking...

"Don't I have to know how to code to become the technical founder of a $100M project?"

No!

ByteBot comes with sensible defaults and example components that work out of the box³ so you can focus on the stuff that matters - the Telegram group and the Twitter raids! 🚀🌕🤑

³Editor's Note: Yes there are defaults to get started but they honestly are shitty and stupid and if you use them you're a massive cuck and you have deep fundamental skill issues.

Over time, life will punish you relentlessly for seeking the easiest, least meaningful path in every situation.

ByteBot is literally built to be built - it's created for customization and composition. Don't be a fucking idiot.

Benefit #3: Multi-Agent "Swarms"

"Multi-agent? You mean like my ByteBot agent can talk to other ByteBot agents on Twitter?!"

No! Not at all! I mean, yes, sure, you could do that I guess but that's absolutely fucking stupid and trivial! Try again!

"Woah! Okay! Multi-agent... you mean like those invite-only Discord servers filled with two-dozen kinkbots and the tail-wearing Renaissance Fair incels that write their sexually violent system prompts?"

...okay, no, that's wrong again. Let me just tell you!

ByteBot defines many sub-agents for key services of apps that benefit from LLM calls (which is really not a long list... ...yet). More importantly, the sub-agents that ByteBot defines all implement a standard for writing new sub-agents that are compatible with all ByteBot components and systems.

Any time you have a problem that requires an LLM to assess the situation or determine the right output (which is more rarely than you think), developers will have a fully compatible guideline and interface for spinning up a highly specialized sub-agent.

And, because LLMs have unreliable outputs, sanitization and quality assurance protections are built into/inferred by all base classes, system dependencies, and interfaces. Is literally impossible for slop to reach the outside world (and far less likely for tool calls to run up $100k in API credits).

So, if your agent-powered app encounters a novel data type or schema when dealing with a new blockchain VM...

...are the furry-bots writing Erowid trip reports (in fake terminal UIs, for some reason) gonna help you with that shit?

But, a ByteBot SQL/DDL agent can help you with that. The right agent enables dynamic adaptation for applications with reasonably benign failure modes.

"The craze thats sweeping the technology sector and turning heads from here to Hong Kong - they're calling it, 'building things that are economically productive', and it could change Web3... forever."

Benefit #4: A Black Market for AI Agent Body Parts

Imagine you and "the homies" spend a couple months building a context system for a high-level "Orchestation" agent that manages a large process in your application...

You ideated and built a groundbreaking approach - the perfect way to compose a context prompt - that actives all the optimal neurons and provides all the necessary background information to execute on the task at hand.

Its your secret recipe; a healthy base of current convo with a user, steeped in any past conversations with other users about the same specific subject, topped with a king-sized portion of the latest info from web search distilled through a multi-agent summary and enrichment system...

...all infused with the sentiment analysis of the last n reflection > analysis > decision > action > outcome loops...

Well. Get ready to 👏 absolutely 👏 fucking 👏 print! 🤑🌕🚀

ByteBot defines a standard for an open marketplace and usage agreement for ByteBot-compatible components, allowing builders to offer their unique agent parts directly or over API.

That means you and the shkwad can keep your secret rocket sauce in the salami silo and define your own terms for use and compensation; tokens, in-kind components, feet pics, whatever you want!

This how we finna make bands, doggie...

A black market services protocol sharing components between teams and, more importantly, allowing multi-agent applications to upgrade and extend their own functionality without developer intervention.

The goal of this project is to provide an open-source standard that defines input and output interfaces for agentic applications and their components.

ByteBot will be comprised of:

• a standard specification developers can adhere to, ensuring compatibility and interoperability with other ByteBot application components
• a library and set of base components which can be used directly as a package or manually composed and extended
• comprehensive documentation, examples, and tutorials

Most importantly, ByteBot will be designed with a focus on helping real developers do hard, inimitable shit with LLMs.

Thinkin’ Of A Master Plan

Our goal is to release ByteBot along with a standard library (no, not a “standard library”, a “standard” library) for JavaScript and Python in the 2nd half of 2025. We hope to extend the standard library to other languages - main targets being Rust and Go - sometime after the initial release.

To test, tweak, and validate the standard, and to play fun games with your emotions, we will be launching two multi-agent applications in stealth over the coming months.

We will launch these two applications as our alpha and beta proof of concept - one after the other.

You remember when Kill Bill came out and then Kill Bill 2 dropped like right after?

It will be almost exactly like that.

We believe this approach is the best way to:

• Develop, test, and validate in the open
• Make many people embarrassed about how giddy they were over the slopbot era
• Form a hive-mind cult protected by a thick membrane barrier of Asperger’s syndrome and severely atrophied social masks

To innocent bystanders on the internet, these applications will each look like one of the personality.txt Twitter “agents” you psychos pumped to literally three hundred and fifty million united states dollars.

But, they are actually many agents woven together with application logic.

Like, look at the device you’re reading this on. You see a device, right?

Wrong, dumbass. It’s trillions of atoms. You look so stupid right now.

You are free to interact with these applications during the testing phase (if you can find them).

Most of the components used by these multi-agent applications will come in the standard library for the ByteBot anti-framework framework I mean interop standard. However, we will be implementing a limited set of complex, opinionated components show what is possible in the language of the standard.

Is also a nerd snipe mechanism.

These more advanced components will be included as the first collection that is available on the ByteBot market place.

We will be giving away 10,000 free lifetime licenses to the components. We will not tell you how to get them. Is an engagement farming I mean cult initiation mechanism.

Additionally, any development team who:

• submits a component that is compatible with the ByteBot standard
• has their component listed in the ByteBot marketplace
• has their component used in a ByteBot-standardized application (by another team, skemmer)

...will be eligible for a free lifetime license to all components we develop and publish.

The aforementioned non-standard components used in our pilot agents will be available in the ByteBot marketplace at launch - courtesy of Baron Byte (iykyk). You’ll have to mint access to them, like an NFT. I know what you’re thinking: “that’s fucking, SO stupid”. Yeah, it is, actually.

The following is a brief summary of the genesis collection components:

Baron Byte's Components Collection

Context Management & Worldview

The Problem: Current AI agents say the corniest, cringiest slop bullshit you've ever heard it's honestly EMBARRASSING. That's because we're used to people-talk. People-talk is, in most cases, a function of:

• A reasonably stable and consistent personality (see: Alex Jones)
• Whatever mood the people is in
• What the people knows about the current topic
• Past discussions about the topic
• Related topics adjacent to the current topic
• Unrelated topics that share semantic configurations with the current topic ("analog space", something LLMs are dogshit at currently)
• Cultural references from every decade they've been alive
• How they've talked to you in the past
• Lies that they have to maintain when talking to you
• How you talk
• How people you are similar to talk
• How the people they want to be would talk if they were here

Current agents don't do any of this. That's why they always sound like feds.

The Solution: Our component kit includes a Context Management & Construction system. Agents using this component will be calibrated with a rich, human-like worldview and context so that your outputs don't sound like the literal cops.

Global Default Worldview

• Is like if personality.txt and character cards weren't shitty and stupid. Functional and dynamic; actually evolves from experiences - not just LARP lore a memecoin dev ChatJippity'd into an input box
• Uses multiple lower-level model calls to apply worldview bias to other forms of context and strategize probable opinions/reactions

Event Delta/Emotion Layer

• Uses more lower-level model turns to analyze past n reflection/analysis/decision/action/outcome turns and determine likely emotional state
• Applies semantic bias to topics and opinions based on emotional analysis

Topic-Specific Historical Context

• Models the topic of current conversation and >n-character messages, produces embeddings, then runs semantic search for all past conversations with any user about the topic
• Pulls topic-relevant documents from past research and knowledge base
• Doesn't chunk, embed, and store 15kb vectors for every goddamn three words the system ever receives as input
• Doesn't just pretend to know things

Social Context

• Pulls past conversations with same users into the context and extracts relevant memories for use in context window
• Remember peoples biases, politics, preferences, names, foot size, address, etc…
• Pulls conversations with other users about the same topic into the context to help agents understand the topic

Context Looping

• Dynamically generate/extend temporary system prompts from verbose user or agent instructions
• Lock context segments in the system prompt until a task is completed
• Never feel like an agent forgot everything 3 messages after you gave it very specific instructions

Memory Management

The Problem: Current approaches to memory are basic — just storing user-assistant turns in JSON structures or vectorizing data for semantic search. This rigid structure makes it impossible to create nuanced, task-specific memories or retrieve them effectively.

The Solution: our component rethinks memory with a multi-agent memory management system. Different agents handle distinct types of memory:

• Conversational Memory: Logs of user-agent exchanges fully normalized so you can reference individual messages or full conversations as needed (without redundancy)
• Specialized Storage: Separate schemas, routes, and, optionally, databases; each optimized for the data category - blockchain data (e.g., transaction histories), research data (e.g., web-scraped articles), conversational data, etc
• Summary Agents: Leverage LLMs to summarize both instance memory and long-term memory with variable scope and magnitude (instead of all of your agent’s memory being chains of user/assistant conversational turns)
• Data Analysis Agents: Handle large, complex datasets like time-series blockchain data or transaction logs, optimizing how this information is retrieved and integrated into workflows.

This modular approach ensures every type of memory is stored, processed, and retrieved in a way that’s tailored to its specific use case.

Output Construction

The Problem: When interfacing with users or generating social media content, AI agents produce outputs that are corny, robotic, and feel like they were written by a telegram scammer. Even when they get the facts right, they fail to engage users because their tone and style give away that theyare fucking aliens.

The Solution: use an agent-powered multi-layered process for constructing outputs:

• Raw Output Construction: Generate a response with all the relevant facts and information, even if it’s overly detailed or messy.
• Fact Refinement and Filtering: Pare down the response to focus on what’s actually necessary and relevant.
• Linguistic and Dialectic Translation: Analyze the linguistic norms, tone, and dialect of the target audience and adapt the response to match.
• Cultural and Social Context Integration: Incorporate memes, cultural references, and idiomatic expressions to make the response relatable and engaging.
• Final Humanization: Ensure the output feels natural, relatable, and free of the awkward, robotic tone that plagues current agents. Leverage multiple refinement strategies (including Find The FedTM Lexical-Dialectic-Semantic Analysis)

This approach makes agents better at both understanding and generating culturally relevant content.

Cultural Awareness

The Problem: From time to time, user-heavy applications may want interfacing agents to use memes or cultural references while engaging with users. These memes or references are usually outdated, poorly executed, or dropped into the wrong context entirely. Worse, they often fail to understand memes or references in user inputs, leaving them confused and causing a devastating hit to their perceived rizz.

The Solution: our component dedicates a specialized module to cultural awareness:

• Memetic Knowledge Base: A constantly updated database of memes, cultural references, and inside jokes, including their meanings and appropriate use cases.
• Referential Humor Module: Agents can incorporate humor tied to cultural or historical references, enhancing relatability.
• Contextual Relevance Mechanism: Ensures that cultural references are appropriate for the specific audience and context of the conversation.

This approach makes agents better at both understanding and generating culturally relevant content.

On-Chain Tooling

The Problem: Honestly most on-chain tools are probably written fine enough and as models get smarter they’ll be able to perform smoothly where they previously had little quirks or whatever. Transactions will stop failing. Schema adherence will improve. Models will be able to ensure complete requirements for tool use or even write and run their own functional tools. Shit like that.

That being said, a lot of the more advanced slopbots coming out today are geared towards an agentic wallet buddy or whatever. Their transactions fail a shitload. You probably don’t need an AI agent to give you a swap modal in some shitty chatbot UI. So, the tools out there don’t do a whole lot yet.

The Solution: The initial components kit will come with, just, a shitload of primitives and a framework for composing on-chain transactions: To start, we will look to support:

• Base: Because fat, heavy, bald bags and coins that can’t go down despite how exasperatingly retarded they may be
• Solana: Obviously
• Monad: gmonad
• Eclipse: gsvm

Support for additional chains will be added at a schizo-autistic pace.

Here’s what you can expect from our approach:

• Very Basic Building Blocks That Fit Together Nicely: We will provide exhaustive primitives that you probably don’t need to know about except that it will facilitate any kind of on-chain transaction or query for a given chain
• Transaction/Research Composition Framework: Each primitive will be accessible by a standard interface so you don’t have to remember the syntax for a bunch of different shit and you can also write your own primitives. Most importantly, you can define bundles/hierarchies of those primitives - so you can compose contract function workflows similar to swap routers or even pull and interpret ABI/IDL for any contract before interacting with it.
• DDL Schema’s For Each Chain: Two words: SQLite and PostgreSQL (technically 8 words). If you don’t want to deal with data modeling, normalization and DDL schema, you can just yeet the defaults. They’re solid, and the store/retrieve logic is standardized so you don’t have to deal with the crazy join ops you just request what you want to see or store.
• Solver Agent: So, this is one of those things it’s convenient to have an LLM w/ web search for: we provide a Solver Agent with a toolkit of sub-agents and functions. The agent can ultimately use the primitives to plan out the solution to on-chain intents. Now your slopbot (or users) can just ask for what they want and get it optimally. No fees.
• Crowdsourced Expansion: We leave this component in the hands of the buidlers. Any team can write the same list of primitives (and add new ones) for a different chain and make it available in the black market. You can also tweak the solver architecture. Do whatever you want dog is open source.

Workflow Management

The Problem: Workflows are sequences of steps that are triggered by an event. The steps could be invoking an agent that cascades down a lot of substeps, or invoking a function. Triggering events can be scheduled tasks (cornjobs), external data payloads (someone replied to your tweet with “ur gae”), etc.

Today’s agents are built to execute pre-defined workflows. These kind of like party tricks - they’re cool when you first see them and people are impressed but then you quickly realize they’ve become your whole identity. Turns out everyone actually hates you and you’re a loser.

The Solution: ByteBot equips agents with dynamic workflow construction capabilities:

• Default Workflow Inheritance: Provides robust default workflows that agents can modify or build on.
• Menu of Workflow Modules: Agents can see the available modules and use them to construct new workflows on the fly.
• Dynamic Trigger Handling: Enables agents to respond to novel triggers by creating corresponding workflows in real time.

Managing Your Crackheaded Expectations

We will be releasing the first ByteBot application imminently (that means in weeks not months like maybe 2-3 weeks but also don't want to absolutely fuck ourselves by giving you a solid date so we use functionally vague term which implies immediacy as a clever alibi).

Shortly prior to the release of this application, the time to be 🟢 early will be over and this website will be changed significantly. If you're reading this, you're probably already 🟢 early - congratulations.

"How Can I Help?"

You honestly can't help us we're too far gone at this point. All the ways you could have intervened on the path our lives took would be ineffective this late in the game.

"Should I just shill it?"

No. That's honestly way too much pressure. People will find out you don't need to shill it.

"Is there some way I can get involved?"

Oh yes actually there is. So, if you found the Discord, you can contribute by making more of these:

This alfa will self destruct in 48 hours.

Thank you for your time and attention.

Sincerely,
The ByteBot Assimilative Cluster

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