Lead Engineer/Programmer with Python Expertise in Habibot

@Fazeennazar

Hi there, I have checked your requirement carefully and gone through. I think that I can help you to complete this project 100% perfectly sure to satisfy your requirement. I appreciate if you give me an opportunity contact once to discuss this further. I would love to build an awesome working relationship with you using my expertise. Thank You!

@intelliresponse

I’d take M1 first because getting the restaurant flow working end-to-end is the foundation for everything else. I’d start by understanding the existing Python/aiogram codebase and test suite, then focus on completing the real discovery → booking request → venue response → confirmation → reminder loop and making sure requests can’t get stuck. I’m comfortable working with existing code rather than rewriting it unnecessarily, and I can handle Python backend services, APIs, bots, webhooks, integrations, and security-focused development. I’d also be comfortable taking on M2/M3 after M1 is proven, including learning and implementing the P2P/Hyperswarm component. I can commit around [X] hours/week for the next 90 days. I’m also comfortable with the equity/profit-share structure and the expense reimbursement arrangement described in the post. I’d be happy to review the existing repository and current M1 status before committing to a detailed implementation plan.

@sagarjain4010

From what I've understood, Habibot already has the core booking engine, state machine and test suite, and you need a lead engineer who can take ownership of the existing Python codebase and turn the remaining pieces into a reliable live product. I’d take M1 first: real discovery → venue request → confirm/decline/counter → booking confirmation → reminder, with particular attention to eliminating stuck REQUESTING states and keeping async flows reliable. I’m comfortable extending existing systems rather than rewriting working components, and can then build toward Tavolata, P2P replication and additional booking verticals as traction validates them. A few points to confirm: • How is the current vendor confirmation loop implemented? • How many hours/week are you expecting initially? • Can you share the current test/development environment before starting? I’m comfortable with the equity/profit-share structure provided the terms and IP are documented upfront. Please note: The budget and timeline in my bid are placeholders. If the codebase is ready for review, I can start by auditing M1 and identifying the shortest path to a real pilot. Regards, Sagar

@dvcontact

Hello Sir, Are you ready to elevate Habibot with a next-gen booking assistant while ensuring quick deliverables from an extensive existing codebase? My deep expertise in Python and experience with asynchronous services uniquely position me to close critical gaps while enhancing security and reliability. Let's discuss how I can take ownership of the next milestones and drive Habibot's growth together. Best, Smith

@denisSolution

Hi, I'm Denis, a full-stack developer with years of experience building production Python services, async workflows, and bot integrations including Telegram bots, webhook systems, and multi-channel platforms where reliability and speed matter. I understand Habibot: it's a booking system disguised as chat—the hard part isn't UI, it's keeping state consistent across async operations, handling vendor confirmations that may never arrive, and scaling from one city to many without rewriting. You've solved the core logic. What's left is closing loops that keep requests stuck, adding growth through conversation marketing, and securing the booking ledger with P2P replication. I'd prioritize M1 first. It's the proof point. A working restaurant MVP in one city with real venues and diners tells you whether the premise works. Once validated—diners book, venues confirm, reminders fire—everything else becomes pattern. M2 and M3 are extensions, not bets. My immediate focus: finishing the vendor delivery loop so REQUESTING states resolve, hardening the state machine under load, and ensuring notifications don't drop confirmations. These aren't glamorous, but they separate demos from products. I'm comfortable with equity plus expenses if commercialization doesn't happen, and happy to ramp intensity as traction grows. Part-time is realistic initially. I can start right away. Let's discuss the details. Thanks, Denis.

@PINESCRIPTERR

Hi, I would take M1 first because the existing booking state machine and slot-lock foundation already appear to cover much of the core logic, so the priority should be closing the live vendor delivery loop, eliminating stuck REQUESTING states, and proving a complete real-world Telegram booking flow before adding more verticals. I’m comfortable extending existing Python async services, Telegram bots/webhooks, APIs, state machines, and production integrations without unnecessary rewrites, with a strong focus on reliability, observability, and secure handling of booking data. I’d then use the proven reservation engine as the foundation for Tavolata, P2P replication, outreach, and additional bookable verticals. 1. Which part of the existing vendor delivery flow is currently responsible for REQUESTING remaining unresolved, and do you already have a standard confirmation/decline/counter webhook or API contract from pilot venues? 2. For the Hyperswarm mirror, should it act as an immutable/auditable secondary reservation store while the primary booking state remains in the existing database, or do you expect active recovery/failover directly from the P2P layer? 3. What level of weekly availability are you expecting from the founding engineer during the initial M1 phase, and which existing tests/environment can I use to validate the live booking loop before the pilot? Regards, Houssame

@TechizeBuilder

Dear Client, I read "Lead Engineer/Programmer with Python Expertise in Habibot" carefully and understand you need hands-on Claude/Anthropic expertise to build or integrate an AI assistant that actually works reliably. My hands-on experience with PHP, Python, Android, Chatbot aligns directly with what you need. I've built production integrations with the Claude API — prompt engineering, tool use, agentic workflows and RAG pipelines — tuned for accuracy, cost and latency, and I can bring that same rigor to your project. A few quick questions to get us started: 1. Which Claude model and use case are you targeting — chat assistant, agent, RAG, or something else? 2. Do you have existing prompts/data, or should we design the pipeline from scratch? 3. What's your expected usage volume, so we can plan for cost and rate limits? Thanks & Regards, Deepak

@pinesucceed01

Hello, My name is Neeru and I'm a highly experienced software developer, fluent in multiple programming languages such as Python, PHP, and JavaScript as well as familiar with frameworks, from WordPress to Django. With 16 years in web and software development, I know what it takes to not just write great code but build efficient and robust systems - which fits perfectly with the next steps in your Habibot project. Having worked with large codebases before, I can easily navigate through existing architecture, understand its nuances and extend it without unnecessary rewriting, an essential quality for your project. In addition, my proficiency with RESTful APIs and understanding of the multilingual layer aligns well with milestones like city density "Tavolata" implementation. Also, my experience in delivering API-connected software services will allow me to successfully complete M2-M3 milestones where you require a multi-channel outreach. Lastly, I am perfectly comfortable working part-time at the start with an increasing intensity as traction increases. This not only makes me adaptable but also ensures that no aspect of the project goes neglected while not compromising on quality or delivery speed. The success of your Habibot rests on efficiency, reliability, adaptability and ownership - these are qualities I strive to maintain every day. Let's collaborate to bring your vision into an even brighter future! Thanks!

@AndrewJSMAN

I’ve built and scaled Python bots with aiogram, so this is right in my wheelhouse. I’ll start with M1 (vendor delivery loop + Telegram confirmation flow), then Hyperswarm integration for M2, and finally extend to verticals for M3. I’ll wire the missing pieces—webhooks for live discovery, Telnyx voice/STT, and P2P booking mirror—without rewriting. The existing state machine and tests will reduce validation time. I’ll document the Hyperswarm setup and outreach hooks for handoff. I’m available for 30h/week now, ramping with traction. I’m OK with equity or reimbursement only. I can start immediately. Thanks, Andrii.

@arslanshahid386

Hi, I noticed that Habibot is a chat-first booking assistant currently focusing on Telegram, with plans to expand to WhatsApp and voice. Given your existing large codebase, I can leverage my expertise in Python and software architecture to efficiently manage and ship the next product milestones. My experience with building scalable and secure applications will ensure that we can enhance the current functionalities and integrate new features seamlessly, keeping security as a priority. Could you share more about the specific milestones you aim to achieve next?

@cliftonc20

Hi, Habibot’s focus on completing and securing a complex booking assistant with real-time confirmation and reminders aligns with my experience building robust asynchronous Python services and orchestrating multi-step workflows. The main technical risk is ensuring data consistency and reliability in the booking state machine and the P2P replication layer under live conditions. I typically design systems by separating ingestion, state management, and notification layers, allowing independent scaling and easier debugging. My approach would validate the booking confirmation flow early to avoid stuck states. In prior projects like DocIntel AI, I built asynchronous pipelines with FastAPI and PostgreSQL that required strict consistency and fault tolerance, similar to your slot-lock and notification router. Dent-Cloud’s scalable SaaS architecture also informs my approach to multi-vertical booking and growth layers. I recommend starting with a detailed review of the booking state machine and replication design to outline the first technical milestone. Thanks, Clifton

@melikatfz

Hi there. Taking over an existing codebase often leads to the trap of rewriting everything from scratch, which wastes precious time and delays your restaurant MVP launch. My solution is to immediately step into your Python and aiogram stack, wire the missing vendor delivery loops, and lock down the M1 milestone without touching what already works. Here are my questions regarding the project scope: 1. Are the current venue communication endpoints for the confirm or decline loops already documented or will we need to integrate specific vendor APIs from scratch? 2. For the Hyperswarm P2P mirroring in M2, do you have a preferred schema structure in mind for the replicated reservation records? Let's discuss your project now!

@mughiraaa

hi, i’d take **m1 first** because getting the restaurant booking loop fully live is the foundation for everything that follows. the core logic already exists, so i’d focus on completing the vendor confirmation flow, live discovery, telegram booking, confirmations and reminders without rewriting what is already working. i can work directly with the existing python and aiogram codebase, async services, apis and webhooks, while keeping security and reliability in focus. once m1 is proven with real venues and diners, i can help move into tavolata, p2p replication and additional booking verticals. i’m comfortable joining at part time initially and taking real ownership of the product rather than working ticket by ticket. i’m also open to the equity and expense reimbursement structure. can we have a quick meeting to go through the current codebase, m1 blockers and your vision for the next 90 days? i can share relevant work and discuss how i’d approach the first milestone during the call. mughiraa

@KarthikTechCon

Hello, I’m Karthik, a seasoned engineer with 15+ years in software development, particularly in Python. I’m excited about the opportunity to contribute to Habibot as a Founding Engineer. I propose starting with M1 to establish a robust restaurant MVP on Telegram, ensuring seamless end-to-end booking and confirmation processes. My experience in developing production services and working with APIs and bots will allow me to effectively tackle the existing codebase and drive the project forward. I have a strong background in building scalable systems and a passion for ownership and delivering results. Additionally, I'm comfortable working part-time initially, ramping up as traction builds. I can commit 15 hours/week for the next 90 days. I’m fully aligned with equity compensation and reimbursement terms. Let’s build something great together! Looking forward to your response. Best, Karthik

@rosscarter1011

Hi there. Taking over an existing codebase with incomplete vendor loops and complex state machines often leads to unexpected regressions and broken booking flows if not handled with precise architectural discipline. My expertise in robust Python async services and production-grade Telegram bots ensures I can seamlessly extend your current system without unnecessary rewrites, closing the remaining gaps in your vendor delivery loop and security layers efficiently. Here are my questions regarding the scope: 1. What is the current state of the vendor response interface for handling confirms, declines, and counter-offers outside of the core bot? 2. Are you currently using a specific database backend for the signed ledger that we need to maintain compatibility with during the Hyperswarm P2P integration? Let's discuss your project now!

@lazarr4

Hello, I can help ship M1 by extending the existing Python bot to handle real-time booking confirmations and reminders, while avoiding common pitfalls like stuck requests. I’ve built a similar Telegram bot for a restaurant reservation system where I added a confirmation flow that reduced no-shows by 30% by sending immediate SMS reminders. I’d approach this by adding a webhook-based confirmation system to the existing aiogram bot, then integrating with a calendar API for reminders. The biggest improvement will come from closing the vendor delivery loop and ensuring bookings don’t get stuck in REQUESTING. This will improve reliability and reduce support requests by making the booking process seamless from start to finish. I’ll also document the security implications of the Hyperswarm integration to ensure bookings are tamper-proof. Once the project is complete, I’ll provide a handover guide and help debug any issues that come up during the pilot. If this sounds helpful, I’d be happy to discuss how we can extend the existing system to meet your milestones. Thanks, Lazar.

@carlosd178

Hello, I reviewed Habibot’s current state carefully, and I like that this is about finishing and operating an existing product rather than rewriting a working codebase. My background includes Python, async services, APIs/webhooks, bot development, security, and production integrations. I would take M1 first. The booking state machine already exists, so the highest-value work is closing the live loop: replace placeholder discovery with real sources, make venue request/confirm/decline/counter reliable, eliminate stuck REQUESTING states, and verify reminders are idempotent. That gives us a measurable real-world booking path before adding Tavolata density, Hyperswarm replication, or new verticals. For M2, I’m comfortable extending the existing architecture rather than replacing it, including Hyperswarm-backed reservation mirroring with recoverability/auditability and multi-channel integrations such as Telnyx/Twilio. I can commit approximately 20 hours/week for the next 90 days, with flexibility to increase as traction grows. I’m also comfortable with the equity/profit-share model and capped expense reimbursement if nothing commercializes, provided the IP, equity, vesting, and reimbursement terms are documented before deep work. My first technical question: how is venue confirmation currently delivered in the incomplete vendor loop—Telegram, SMS/voice, web link, or multiple channels? Best regards, Carlos

@emilesd617

Hi, I am a full stack python developer with 8 years of rich experience in software development, with a background in production services, chatbots, API integrations, automation, and scalable backend systems. I am familiar with Python, async development, Telegram API, aiogram, APIs, webhooks, chatbot development, software architecture, security, and AI integrations. For Habibot, I would start with M1 and focus on closing the live restaurant booking loop without rewriting the existing codebase. I can trace the current booking state machine, vendor response flow, discovery sources, notifications, and Telegram webhooks, then complete the missing confirmation and reminder paths with proper error handling and tests so REQUESTING states do not remain stuck. I can then build on the existing architecture toward the later multi channel and booking requirements. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@ianb65

Hi there, Habibot’s core booking logic is already strong; the immediate engineering challenge is closing the live vendor confirmation loop so requests cannot remain REQUESTING, while preserving reliability and security across the existing Python bot. I have strong hands-on experience with Python and Software Architecture and have solved similar problems involving asynchronous production services, webhook-driven workflows, and stateful booking integrations. My approach would be to: * Review the existing code, architecture, logs, acceptance suite, and current implementation. * Identify the root cause and determine what can be reused versus what needs to be changed. * Implement a clean, maintainable solution using Python async services, aiogram, webhooks, and the existing booking state machine. * Test edge cases, reliability, performance, security, and integration behavior. * Deploy or integrate the fix without disrupting the existing system. I would take M1 first because a real restaurant pilot validates the end-to-end product and creates the operational foundation for Tavolata, outreach, and multi-vertical expansion. Idempotent venue responses and durable state transitions are essential so retries cannot create duplicate confirmations or reminders. I prefer fixing the actual root cause rather than applying temporary patches, and I will keep the implementation simple, stable, and maintainable. I can start by reviewing the repository, logs, acceptance tests, and current v