Finally, a better way to pay back your loans. Scratch is reimagining loan servicing to help borrowers understand, manage, and pay back their loans.

Scratch | Loan servicing reimagined

Scratch is changing the way people experience debt.

Scratch - Crunchbase Company Profile & Funding

We’ve also developed a Paycheck Protection Program (PPP)-specific platform to help you guide your small business borrowers through PPP loan forgiveness and servicing. Scratch is proud to be a Great Place to Work-Certified™ company: https://www.greatplacetowork.com/certified-company/7025478 ...

Scratch | LinkedIn

Scratch is a free programming language and online community where you can create your own interactive stories, games, and animations.

Scratch - About

I was ghosted after initial screening (and no response to my questions); very unprofessional to not respond to a candidate after the interview. This company has a poor recruiting infrastructure. ... All answers shown come directly from Scratch Services Reviews and are not edited or altered.

Working at Scratch Services | Glassdoor

The employee experience below at Scratch, compared to a typical company. Learn More · 93% of employees at Scratch say it is a great place to work compared to 57% of employees at a typical U.S.-based company.

Working at Scratch | Great Place To Work®

Scratchpay provides simple and friendly, payment plans for medical financing. High approvals, no hidden fees, no surprises.

Scratchpay: Simple & friendly, payment plans for medical financing

We’re sorry but our site doesn’t work properly without JavaScript enabled. Please enable it to continue · Loading

Scratch San Francisco Office: Careers, Perks + Culture

Information on valuation, funding, cap tables, investors, and executives for Scratch. Use the PitchBook Platform to explore the full profile.

Scratch Company Profile: Valuation & Investors | PitchBook

Scratch - Search

Scratch has 5 employees at their 1 location and $17.25 m in total funding,. See insights on Scratch including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Scratch Company Profile - Office Locations, Competitors, Revenue, ...

A collaborative ensemble restaurant on Pittsburgh’s North Side. Featuring a local seasonal breakfast, lunch, dinner, and bar menus with international flavors. Scratch is well known for their shared plates dining experience and community programming that includes Pittsburgh based artists, magic, po

Scratch & Co.

Specialties: Hello! Scratch &amp; Co will be closed for the month of february. We will be back on saturday march 5th. feel free to reach out to us at hello@scratchandcopgh.com. Thank you for your support and see you soon! Given the recent blooming of Covid-19 cases in Allegheny County, we will ...

SCRATCH & CO | 448 Photos & 262 Reviews | 1720 Lowrie St, Pittsburgh, ...

See Instagram photos and videos from Scratch &amp; Co (@scratchco_pgh)

Scratch & Co (@scratchco_pgh) • Instagram photos and ...

The company I was with was awesome and the evening was very nice.More ... This review is the subjective opinion of a Tripadvisor member and not of Tripadvisor LLC. Tripadvisor performs checks on reviews as part of our industry-leading trust &amp; safety standards. Read our transparency report to learn more. ... For our weekend foodture we went to Scratch...

SCRATCH & CO, Pittsburgh - Menu, Prices & Restaurant ...

Mahaney just made his next pivot, rebranding his business as Scratch &amp; Co. to better represent the community aspects — including farmers, collaborators and customers — that encompass the business. He’s spinning off a provisioning company, Scratch &amp; Co. Made Brand, with Kevin Klingensmith ...

How Scratch & Co. Is Embracing Flexibility | Pittsburgh Magazine

Learn about PaintScratch.com. Making top quality custom car paint since 1999 to repair scratches and make your car look great.

About PaintScratch Touch Up Paint - PaintScratch.com

Get menu, photos and location information for Scratch in Pittsburgh, PA. Or book now at one of our other 3253 great restaurants in Pittsburgh.

Scratch Restaurant - Pittsburgh, PA | OpenTable

Make games, stories and interactive art with Scratch. (scratch.mit.edu)

Scratch-Company on Scratch

Get the most recent info and news about Scratch on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

Scratch

For the last 2 months, I've been intensively building a Bitcoin wallet from scratch as the MVP for my startup, Nummus. It's been quite the journey so far, and I'm making substantial progress. This post serves as an update as I complete the first major development milestone. I wanted to share how I got started, what I've accomplished, and what's coming next before the launch. What I'm Building Nummus will be a custodial wallet that offers a BTC-backed credit card, collateralized loans, and secure custody. You will be able to hodl your bitcoin while using its value on a card - solving the paradox of "never sell your Bitcoin" and "Bitcoin is the future of money", allowing you to spend it everywhere mastercard / visa is accepted. I wrote a separate article on this here. Nummus will be Bitcoin + stablecoin only with native Bitcoin infrastructure – no reliance on sketchy third parties like FTX (looking at you, BlockFi). While I can't build the entire platform alone, what I can accomplish independently is developing a solid wallet. This MVP is non-custodial and focuses on base layer Bitcoin functionality, with Lightning potentially coming later if time permits. The main goal is to ship a clean, functional Bitcoin wallet that demonstrates my ability to deliver, grow, and scale a product to both users and potential investors. How I Prepped for This I've been a frontend developer for almost 7 years, and recently obtained my backend certification from Primeagen's online bootcamp after dedicating 8 months to the program. I'm now confidently full-stack and satisfied with my programming capabilities – particularly after becoming addicted to Factorio, which significantly enhanced my problem-solving skills after investing 250 hours in a single month. I originally set a goal to find a CTO by January 20th and was feeling kinda hopeless about doing this alone. Then I had a realization – why not be my own CTO? I decided that day I'd just wear both the CEO and CTO hats. How hard could it be, right? I'm already technical. To enhance my skills, I immersed myself completely in the learning process. I enrolled in several online courses focused on mobile development with React Native – leveraging my existing React knowledge made the learning curve relatively manageable. Within a week, I had grasped the fundamentals and developed several test projects. The most valuable resources were React Native by CodeWithMosh and PlebDev's backend wallet course (Shoutout to Mr. PlebDev aka Austin K – his curriculum is excellent). I also decided it was time to go deeper on Bitcoin's technical side. I grabbed 5 O'Reilly textbooks about Bitcoin, Lightning, and blockchain to really understand the tech. Plus I added 3 more O'Reilly texts about software architecture, engineering exec leadership, and building microservices. I'm about 2/7 through these books and study for an hour daily. I even learned Vim the motions and how to use my Mac with JUST keyboard shortcuts to work faster (and now I can't go back). I basically spent a month studying like a monk – all day, every day – everything about app development and Bitcoin. I'm still going deep on Bitcoin as a technology and will probably finish all those textbooks by year-end. Upon gaining confidence in my programming abilities, I discovered Cursor and Claude while developing test React native applications. This marked a significant turning point – my productivity increased tenfold almost immediately. Now I can effectively develop complete applications independently by combining my technical knowledge with AI assistance. Combined with my previous experience building BitEscrow and some performance optimization techniques, my capabilities were well-aligned to engineer and scale this project. I also decided to build in public and livestream my coding sessions, which is actually a ton of fun. On days when I'm not coding or streaming, I'm working on content and distribution like this. Tech Stack After a deep dive on what's out there, I landed on this stack for the wallet: Tool

Category

Why I Picked It



React Native + TS

Frontend

Cross-platform with type safety



Go

Backend

Fast and handles concurrency well



Expo

Framework

Makes mobile dev way less painful



Jest

Testing

Works great with React



GlueStack UI

UI Library

Flexible and perfect for MVP phase



Resend

Mail API

Simple but reliable



BDK-wasm

Bitcoin Lib

Solid Bitcoin functionality



Supabase

Database

PostgreSQL without the hassle If you want to see everything I'm using including all the libraries, check out the GitHub package.json. As much as I'd love to use cutting edge tech like Zig, Elixir, or BunJS, the existing ecosystem for Node and my current stack is already really solid, especially after Microsoft announced they're rewriting the TypeScript compiler in Go. Maybe when Nummus is building its infrastructure, we'll explore Zig or Elixir and Bun. For now, I chose this stack because of how quickly I can build with it, and the combo of TS + Go is pretty tough to beat for an MVP. For project management, I'm keeping it super simple – just Obsidian and markdown using the PARA system. That's it. No bloated software like Jira needed for a team of one. Progress So Far The following screens are functional but currently unstyled. They pass all the tests I've written, and when it comes time to implement BDK, it should go pretty smoothly. The code is organized, modular, and clean. I need to do some refactoring and linting before launch, but things are looking good so far. Wireframing I sketched the wireframes on my e-ink tablet. I already had a pretty clear vision of what I wanted it to look like – just had to get it on paper. Figma Designs From there, I moved the wireframes to Figma and refined the basic designs. I'll circle back and polish the look of the app once BDK is implemented - functionality first. Screens Built So Far: Onboarding: Create or import a wallet Home Screen: Shows your balance with toggles between USD, BTC, and SATS. Has buttons to send or receive bitcoin (these screens are still in progress) Credit Card Screen: Details currently under development and will be revealed later. Bitcoin Screen: Filter by day, week, etc. I'll replace the test API with something better before launch. TX History Screen: Using mock data for now, but you can tap on any transaction to see its details. Roadmap You can view the complete roadmap on GitHub, which I update daily. Here's a summary of the key phases: Basic frontend
Refactor and add tests
Implement real Bitcoin functionality <-- starting today
Write tests for Bitcoin stuff
Make the frontend look really nice
Refactor and test for production
Final checks and security audit
Launch The objective is to have a functional product launched by the Bitcoin Conference Las Vegas in May. Currently, development is progressing on schedule to meet this deadline. Conclusion Building Nummus has proven both challenging and rewarding. What began as an exploratory question about feasibility has evolved into a legitimate product taking tangible form. It's remarkable how much a single developer can accomplish with the appropriate tools and methodology. This non-custodial wallet MVP represents just the initial phase. The broader vision for Nummus is more ambitious – but every significant project requires a humble starting point. The decision to build in public has enhanced accountability and transparency, while the feedback received is already improving the product quality. I'm genuinely enthusiastic about this project's direction, and you're welcome to observe the development process live. Below are links to my social media channels and Discord where you can submit questions and feedback to influence the wallet's development. Want to join the waitlist and get notified when I launch? Sign up here. Links: GitHub
GitHub Project Board
Twitch
YouTube
My Twitter
Nummus Twitter For the last 2 months, I've been intensively building a Bitcoin wallet from scratch as the MVP for my startup, Nummus . It's been quite the journey so far, and I'm making substantial progress. This post serves as an update as I complete the first major development milestone. Nummus I wanted to share how I got started, what I've accomplished, and what's coming next before the launch. What I'm Building Nummus will be a custodial wallet that offers a BTC-backed credit card, collateralized loans, and secure custody. You will be able to hodl your bitcoin while using its value on a card - solving the paradox of "never sell your Bitcoin" and "Bitcoin is the future of money", allowing you to spend it everywhere mastercard / visa is accepted. I wrote a separate article on this here . Nummus will be Bitcoin + stablecoin only with native Bitcoin infrastructure – no reliance on sketchy third parties like FTX (looking at you, BlockFi). here While I can't build the entire platform alone, what I can accomplish independently is developing a solid wallet. This MVP is non-custodial and focuses on base layer Bitcoin functionality, with Lightning potentially coming later if time permits. The main goal is to ship a clean, functional Bitcoin wallet that demonstrates my ability to deliver, grow, and scale a product to both users and potential investors. How I Prepped for This I've been a frontend developer for almost 7 years, and recently obtained my backend certification from Primeagen's online bootcamp after dedicating 8 months to the program. I'm now confidently full-stack and satisfied with my programming capabilities – particularly after becoming addicted to Factorio, which significantly enhanced my problem-solving skills after investing 250 hours in a single month. I originally set a goal to find a CTO by January 20th and was feeling kinda hopeless about doing this alone. Then I had a realization – why not be my own CTO? I decided that day I'd just wear both the CEO and CTO hats. How hard could it be, right? I'm already technical. To enhance my skills, I immersed myself completely in the learning process. I enrolled in several online courses focused on mobile development with React Native – leveraging my existing React knowledge made the learning curve relatively manageable. Within a week, I had grasped the fundamentals and developed several test projects. The most valuable resources were React Native by CodeWithMosh and PlebDev 's backend wallet course (Shoutout to Mr. PlebDev aka Austin K – his curriculum is excellent). PlebDev I also decided it was time to go deeper on Bitcoin's technical side. I grabbed 5 O'Reilly textbooks about Bitcoin, Lightning, and blockchain to really understand the tech. Plus I added 3 more O'Reilly texts about software architecture, engineering exec leadership, and building microservices. I'm about 2/7 through these books and study for an hour daily. I even learned Vim the motions and how to use my Mac with JUST keyboard shortcuts to work faster (and now I can't go back). I basically spent a month studying like a monk – all day, every day – everything about app development and Bitcoin. I'm still going deep on Bitcoin as a technology and will probably finish all those textbooks by year-end. Upon gaining confidence in my programming abilities, I discovered Cursor and Claude while developing test React native applications. This marked a significant turning point – my productivity increased tenfold almost immediately. Now I can effectively develop complete applications independently by combining my technical knowledge with AI assistance. Combined with my previous experience building BitEscrow and some performance optimization techniques, my capabilities were well-aligned to engineer and scale this project. I also decided to build in public and livestream my coding sessions, which is actually a ton of fun. On days when I'm not coding or streaming, I'm working on content and distribution like this. livestream Tech Stack After a deep dive on what's out there, I landed on this stack for the wallet: Tool

Category

Why I Picked It



React Native + TS

Frontend

Cross-platform with type safety



Go

Backend

Fast and handles concurrency well



Expo

Framework

Makes mobile dev way less painful



Jest

Testing

Works great with React



GlueStack UI

UI Library

Flexible and perfect for MVP phase



Resend

Mail API

Simple but reliable



BDK-wasm

Bitcoin Lib

Solid Bitcoin functionality



Supabase

Database

PostgreSQL without the hassle Tool

Category

Why I Picked It React Native + TS

Frontend

Cross-platform with type safety Go

Backend

Fast and handles concurrency well Expo

Framework

Makes mobile dev way less painful Jest

Testing

Works great with React GlueStack UI

UI Library

Flexible and perfect for MVP phase Resend

Mail API

Simple but reliable BDK-wasm

Bitcoin Lib

Solid Bitcoin functionality Supabase

Database

PostgreSQL without the hassle Tool Category Why I Picked It Tool Tool Category Category Why I Picked It Why I Picked It React Native + TS Frontend Cross-platform with type safety React Native + TS React Native + TS Frontend Frontend Cross-platform with type safety Cross-platform with type safety Go Backend Fast and handles concurrency well Go Go Backend Backend Fast and handles concurrency well Fast and handles concurrency well Expo Framework Makes mobile dev way less painful Expo Expo Framework Framework Makes mobile dev way less painful Makes mobile dev way less painful Jest Testing Works great with React Jest Jest Testing Testing Works great with React Works great with React GlueStack UI UI Library Flexible and perfect for MVP phase GlueStack UI GlueStack UI UI Library UI Library Flexible and perfect for MVP phase Flexible and perfect for MVP phase Resend Mail API Simple but reliable Resend Resend Mail API Mail API Simple but reliable Simple but reliable BDK-wasm Bitcoin Lib Solid Bitcoin functionality BDK-wasm BDK-wasm Bitcoin Lib Bitcoin Lib Solid Bitcoin functionality Solid Bitcoin functionality Supabase Database PostgreSQL without the hassle Supabase Supabase Database Database PostgreSQL without the hassle PostgreSQL without the hassle If you want to see everything I'm using including all the libraries, check out the GitHub package.json . As much as I'd love to use cutting edge tech like Zig, Elixir, or BunJS, the existing ecosystem for Node and my current stack is already really solid, especially after Microsoft announced they're rewriting the TypeScript compiler in Go. Maybe when Nummus is building its infrastructure, we'll explore Zig or Elixir and Bun. For now, I chose this stack because of how quickly I can build with it, and the combo of TS + Go is pretty tough to beat for an MVP. GitHub package.json For project management, I'm keeping it super simple – just Obsidian and markdown using the PARA system. That's it. No bloated software like Jira needed for a team of one. Progress So Far The following screens are functional but currently unstyled. They pass all the tests I've written, and when it comes time to implement BDK, it should go pretty smoothly. The code is organized, modular, and clean. I need to do some refactoring and linting before launch, but things are looking good so far. Wireframing I sketched the wireframes on my e-ink tablet. I already had a pretty clear vision of what I wanted it to look like – just had to get it on paper. Figma Designs From there, I moved the wireframes to Figma and refined the basic designs. I'll circle back and polish the look of the app once BDK is implemented - functionality first. Screens Built So Far: Onboarding : Create or import a wallet Onboarding Home Screen : Shows your balance with toggles between USD, BTC, and SATS. Has buttons to send or receive bitcoin (these screens are still in progress) Home Screen Credit Card Screen : Details currently under development and will be revealed later. Credit Card Screen Bitcoin Screen : Filter by day, week, etc. I'll replace the test API with something better before launch. Bitcoin Screen TX History Screen : Using mock data for now, but you can tap on any transaction to see its details. TX History Screen Roadmap You can view the complete roadmap on GitHub, which I update daily. Here's a summary of the key phases: Basic frontend Refactor and add tests Implement real Bitcoin functionality <-- starting today Write tests for Bitcoin stuff Make the frontend look really nice Refactor and test for production Final checks and security audit Launch Basic frontend Refactor and add tests Implement real Bitcoin functionality <-- starting today Write tests for Bitcoin stuff Make the frontend look really nice Refactor and test for production Final checks and security audit Launch The objective is to have a functional product launched by the Bitcoin Conference Las Vegas in May. Currently, development is progressing on schedule to meet this deadline. Conclusion Building Nummus has proven both challenging and rewarding. What began as an exploratory question about feasibility has evolved into a legitimate product taking tangible form. It's remarkable how much a single developer can accomplish with the appropriate tools and methodology. This non-custodial wallet MVP represents just the initial phase. The broader vision for Nummus is more ambitious – but every significant project requires a humble starting point. The decision to build in public has enhanced accountability and transparency, while the feedback received is already improving the product quality. I'm genuinely enthusiastic about this project's direction, and you're welcome to observe the development process live. Below are links to my social media channels and Discord where you can submit questions and feedback to influence the wallet's development. Want to join the waitlist and get notified when I launch? Sign up here. Sign up here. Links: GitHub GitHub Project Board Twitch YouTube My Twitter Nummus Twitter GitHub GitHub GitHub Project Board GitHub Project Board Twitch Twitch YouTube YouTube My Twitter My Twitter Nummus Twitter Nummus Twitter

Building a Bitcoin Wallet from Scratch: Two Months of Solo Development Insights

بناء محفظة بيتكوين من الصفر: شهرين من رؤى التطوير الفردي

Sıfırdan bir Bitcoin Cüzdanı qurmaq: İki Aylıq Solo İnkişaf Anlayışları

Izgradnja Bitcoin novčanika od nule: dva mjeseca solo uvida u razvoj

Construir una cartera de Bitcoin des de zero: dos mesos d'informació sobre desenvolupament individual

Construyendo una billetera Bitcoin desde cero: Dos meses de desarrollo individual

Membangun Dompet Bitcoin dari Awal: Wawasan Pengembangan Solo Selama Dua Bulan

ビットコインウォレットをゼロから構築する: 2か月間のソロ開発の洞察

ການສ້າງ Wallet Bitcoin ຈາກ Scratch: ສອງເດືອນຂອງຄວາມເຂົ້າໃຈການພັດທະນາ Solo

Construindo uma carteira de Bitcoin do zero: dois meses de insights de desenvolvimento solo

“Bitcoin” gapjygyny noldan gurmak: Iki aýlyk ýeketäk ösüş düşünjeleri

Ukwakha i-Bitcoin Wallet ukusuka kwi-Scratch: Iinyanga ezimbini ze-Solo Development Insights

Ukwakha i-Bitcoin Wallet kusuka ku-Scratch: Izinyanga ezimbili ze-Solo Development Insights

Introduction There's one problem when implementing your own chatbot, and that's memory management during the conversation. Of course, you can use ready-made libraries such as 'Langchain', 'Ollama', etc ... But what if you want to implement your own algorithm from scratch? We present here an approach to managing short-term memory in chatbots, using a combination of storage and automatic summarization techniques to optimize conversational context. The introduced method relies on a dynamic memory structure that limits data size while preserving essential information through intelligent summaries. This approach not only improves the fluidity of interactions but also ensures contextual continuity during long dialogue sessions. Moreover, the use of asynchronous techniques guarantees that memory management operations do not interfere with the chatbot’s responsiveness. Mathematical Modeling of Conversation Management In this section, we mathematically formalize the management of conversation memory in the chatbot. The memory is structured as a list of pairs representing the exchanges between the user and the bot. Don't worry, this part's pretty straightforward, just allowing us to model the basic mechanisms so we can transpose them into code. Conversation Memory Structure The conversation memory can be defined as an ordered list of pairs (u_i, d_i), where u_i represents the user’s input and d_i is the bot’s response for the i^th exchange. This list is denoted as C: C = [(u1, d1), (u2, d2), ..., (un, dn)] Where n is the total number of exchanges in the current history. Memory Update When a new exchange occurs, a new pair (u_{n+1}, d_{n+1}) is added to the memory. If the size of C exceeds a predefined maximum limit M_max, the oldest exchange is removed: C = if |C| < M_max:
 C ∪ {(u_{n+1}, d_{n+1})} 
else:
 (C \\ {(u1, d1)}) ∪ {(u_{n+1}, d_{n+1})} Word Count To manage memory space and decide when compression is necessary, we calculate the total number of words W(C) in the memory: W(C) = Σ (|ui| + |di|) Where |ui| and |di| are the number of words in ui and di, respectively. Memory Compression When W(C) exceeds a threshold W_max, the memory is compressed to maintain relevant context. This compression is performed by a summarization model S, such as BART: C_compressed = S(C) Where C_compressed is the summarized version of the memory, reducing the total number of words while preserving the essence of past interactions. Code Implementation for Chatbot Memory Management In this section, we will examine a Python code example that illustrates memory management in a chatbot. The code uses PyTorch and Hugging Face's transformers to manage and compress the conversation history. Environment Setup We begin by checking if a GPU is available, which allows for faster processing if necessary. import torch from transformers import pipeline import logging

if torch.cuda.is_available():
 device: int = 0 
else: 
 device: int = -1

MAX_MEMORY_SIZE: int = 2000 Defining the ChatbotMemory Class The ChatbotMemory class manages the conversation history and performs update and compression operations. Every time update_memory is called, the memory text is counted and processed as needed. class ChatbotMemory: 
def init(self, conv: list = []): 
 self.conversation_history = conv

def update_memory(self, user_input: str, bot_response: str) -> None:
 self.conversation_history.append(f"'user': {user_input}, 'bot': {bot_response}")

 if memory_counter(self.conversation_history) > 1000:
 self.conversation_history = compressed_memory(self.conversation_history)
 logging.info("Memory compressed.")

 if len(self.conversation_history) > MAX_MEMORY_SIZE:
 self.conversation_history.pop(0)
 logging.info("Memory reduced.")
 return 0

def get_memory(self):
 return self.conversation_history Memory Compression and Counting The _get_compressed_memory function uses the BART model to summarize the conversation history. def _get_compressed_memory(sentence: str) -> str:
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device) 
 summary = summarizer(sentence, max_length=50, min_length=5, do_sample=False) 
 return summary[0]['summary_text'] The compressed_memory function applies the _get_compressed_memory function to each segment of the conversation history. This is optimized by processing in batches. This method is separate from _get_compressed_memory to allow for the introduction of new compression methods. def compressed_memory(conv_hist: list) -> list:
 return [_get_compressed_memory(' '.join(conv_hist[i:i+5])) for i in range(0, len(conv_hist), 5)] The memory_counter function counts the total number of words in the history. (Note that it might be interesting to perform this step with tokens instead of words.) def memory_counter(conv_hist: list) -> int:
 st = ''.join(conv_hist) 
 return len(st.split()) Conclusion This code establishes an efficient framework for memory management in a chatbot, using compression techniques to maintain relevant context and improve the overall system performance. Using summarization models like BART ensures that even when memory is compressed, the essential context is preserved. I hope this little demonstration has helped you understand that it's quite simple to model common processes and implement them in your code. It's a basic but essential step that allows you to grasp how things work: 1: I define all the action steps
2: I model them mathematically
3: code! Code on GitHub Introduction There's one problem when implementing your own chatbot, and that's memory management during the conversation. Of course, you can use ready-made libraries such as 'Langchain', 'Ollama', etc ... But what if you want to implement your own algorithm from scratch? We present here an approach to managing short-term memory in chatbots, using a combination of storage and automatic summarization techniques to optimize conversational context. The introduced method relies on a dynamic memory structure that limits data size while preserving essential information through intelligent summaries. This approach not only improves the fluidity of interactions but also ensures contextual continuity during long dialogue sessions. Moreover, the use of asynchronous techniques guarantees that memory management operations do not interfere with the chatbot’s responsiveness. Mathematical Modeling of Conversation Management In this section, we mathematically formalize the management of conversation memory in the chatbot. The memory is structured as a list of pairs representing the exchanges between the user and the bot. Don't worry, this part's pretty straightforward, just allowing us to model the basic mechanisms so we can transpose them into code. Conversation Memory Structure The conversation memory can be defined as an ordered list of pairs (u_i, d_i), where u_i represents the user’s input and d_i is the bot’s response for the i^th exchange. This list is denoted as C: C = [(u1, d1), (u2, d2), ..., (un, dn)] Where n is the total number of exchanges in the current history. Memory Update When a new exchange occurs, a new pair (u_{n+1}, d_{n+1}) is added to the memory. If the size of C exceeds a predefined maximum limit M_max, the oldest exchange is removed: C = if |C| < M_max:
 C ∪ {(u_{n+1}, d_{n+1})} 
else:
 (C \\ {(u1, d1)}) ∪ {(u_{n+1}, d_{n+1})} C = if |C| < M_max:
 C ∪ {(u_{n+1}, d_{n+1})} 
else:
 (C \\ {(u1, d1)}) ∪ {(u_{n+1}, d_{n+1})} Word Count To manage memory space and decide when compression is necessary, we calculate the total number of words W(C) in the memory: W(C) = Σ (|ui| + |di|) Where |ui| and |di| are the number of words in ui and di, respectively. Memory Compression When W(C) exceeds a threshold W_max, the memory is compressed to maintain relevant context. This compression is performed by a summarization model S, such as BART: C_compressed = S(C) Where C_compressed is the summarized version of the memory, reducing the total number of words while preserving the essence of past interactions. Code Implementation for Chatbot Memory Management In this section, we will examine a Python code example that illustrates memory management in a chatbot. The code uses PyTorch and Hugging Face's transformers to manage and compress the conversation history. Environment Setup We begin by checking if a GPU is available, which allows for faster processing if necessary. import torch from transformers import pipeline import logging

if torch.cuda.is_available():
 device: int = 0 
else: 
 device: int = -1

MAX_MEMORY_SIZE: int = 2000 import torch from transformers import pipeline import logging

if torch.cuda.is_available():
 device: int = 0 
else: 
 device: int = -1

MAX_MEMORY_SIZE: int = 2000 Defining the ChatbotMemory Class The ChatbotMemory class manages the conversation history and performs update and compression operations. Every time update_memory is called, the memory text is counted and processed as needed. class ChatbotMemory: 
def init(self, conv: list = []): 
 self.conversation_history = conv

def update_memory(self, user_input: str, bot_response: str) -> None:
 self.conversation_history.append(f"'user': {user_input}, 'bot': {bot_response}")

 if memory_counter(self.conversation_history) > 1000:
 self.conversation_history = compressed_memory(self.conversation_history)
 logging.info("Memory compressed.")

 if len(self.conversation_history) > MAX_MEMORY_SIZE:
 self.conversation_history.pop(0)
 logging.info("Memory reduced.")
 return 0

def get_memory(self):
 return self.conversation_history class ChatbotMemory: 
def init(self, conv: list = []): 
 self.conversation_history = conv

def update_memory(self, user_input: str, bot_response: str) -> None:
 self.conversation_history.append(f"'user': {user_input}, 'bot': {bot_response}")

 if memory_counter(self.conversation_history) > 1000:
 self.conversation_history = compressed_memory(self.conversation_history)
 logging.info("Memory compressed.")

 if len(self.conversation_history) > MAX_MEMORY_SIZE:
 self.conversation_history.pop(0)
 logging.info("Memory reduced.")
 return 0

def get_memory(self):
 return self.conversation_history Memory Compression and Counting The _get_compressed_memory function uses the BART model to summarize the conversation history. def _get_compressed_memory(sentence: str) -> str:
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device) 
 summary = summarizer(sentence, max_length=50, min_length=5, do_sample=False) 
 return summary[0]['summary_text'] def _get_compressed_memory(sentence: str) -> str:
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn", device=device) 
 summary = summarizer(sentence, max_length=50, min_length=5, do_sample=False) 
 return summary[0]['summary_text'] The compressed_memory function applies the _get_compressed_memory function to each segment of the conversation history. This is optimized by processing in batches. This method is separate from _get_compressed_memory to allow for the introduction of new compression methods. def compressed_memory(conv_hist: list) -> list:
 return [_get_compressed_memory(' '.join(conv_hist[i:i+5])) for i in range(0, len(conv_hist), 5)] def compressed_memory(conv_hist: list) -> list:
 return [_get_compressed_memory(' '.join(conv_hist[i:i+5])) for i in range(0, len(conv_hist), 5)] The memory_counter function counts the total number of words in the history. (Note that it might be interesting to perform this step with tokens instead of words.) def memory_counter(conv_hist: list) -> int:
 st = ''.join(conv_hist) 
 return len(st.split()) def memory_counter(conv_hist: list) -> int:
 st = ''.join(conv_hist) 
 return len(st.split()) Conclusion This code establishes an efficient framework for memory management in a chatbot, using compression techniques to maintain relevant context and improve the overall system performance. Using summarization models like BART ensures that even when memory is compressed, the essential context is preserved. I hope this little demonstration has helped you understand that it's quite simple to model common processes and implement them in your code. It's a basic but essential step that allows you to grasp how things work: 1: I define all the action steps 2: I model them mathematically 3: code! 1: I define all the action steps 2: I model them mathematically 3: code! Code on GitHub Code on GitHub

Chatbot Memory: Implement Your Own Algorithm From Scratch

How to Build a Database From Scratch: Understanding LSM Trees and Storage Engines (Part 1)

How to Create Plugins From Scratch in Coze: CoinGecko Case Study

በ Coze: CoinGecko ውስጥ ፕለጊን ከ Scratch እንዴት መፍጠር እንደሚቻል

Cómo crear complementos desde cero en Coze: CoinGecko

Kuinka luoda laajennuksia tyhjästä Cozessa: CoinGecko

כיצד ליצור תוספים מאפס ב-Coze: CoinGecko

Kako stvoriti dodatke od nule u Coze: CoinGecko

Coze でプラグインをゼロから作成する方法: CoinGecko

Coze에서 처음부터 플러그인을 만드는 방법: CoinGecko

Coze-д хэрхэн эхнээс нь залгаас үүсгэх вэ: CoinGecko

په کوز کې له سکریچ څخه د پلگ انونو جوړولو څرنګوالی: CoinGecko

Как создавать плагины с нуля в Coze: CoinGecko

Како направити додатке од нуле у Цозе: ЦоинГецко

Coze'da Sıfırdan Eklentiler Nasıl Oluşturulur: CoinGecko

How to Build a High-Performance TCP Server from Scratch

In the current age characterized by rapid digital growth, companies are faced with a great dilemma when venturing into Web3 technology: should they go for a white-label solution or develop a product from the ground up? Both have their advantages and disadvantages, and an appropriate selection can make all the difference in the success of a particular enterprise. This article explores both approaches, highlighting their benefits and drawbacks. What Are White Label Solutions? White-label solutions” or “white brands” refer to products and services that are created by one company but sold by another company under a different brand name. This approach enables firms to sell products without having to bear the cost of developing them. The key points about it are that it's ready-made products, customizable branding, and facilities for quick market entry. Advantages of White Label Solutions There are also some benefits of white-label solutions which make most of their tasks easier. More economical: There are no development costs, which is very convenient for non-technical companies, media, or influencers. Less time to reach the market: Less time to enter the market. More on brand management: Emphasis is on sales, marketing, and branding. Use Cases of White Label Solutions Some of these white-label solutions can be used across different sectors: E-commerce: Some retailers don’t sell their products, but rather sell the products of third parties under their brand. Software or web3 dApps: Some companies allow others to rebrand their software or web3 dApps and sell them. Financial services: They are not banks but sell banking services whereby their clients’ products are branded with the brand of the other financial service providers. The Appeal of Building a Web3 Product From Scratch There is absolutely nothing wrong with building a product from scratch. In point of fact, there are some benefits to it including: Customization and Control From the perspective of customization and control, creating your own Web3 product is one of the best options. There is how the product is built and that can be made to the tiniest details. This kind of control makes it possible for you to achieve your objectives as you envision. Market Responsiveness The best way to get a Web3 product is by starting its development from scratch, and it is because of the high chances of creativity that it brings on board. You can use one of the most up-to-date technologies and find new applications that cannot be incorporated into white-label solutions. Long Term Vision Building a web3 product from scratch can go along with developing a long-term vision. This involves planning for future growth. It's critical to create a product that evolves with the market. Crucial Considerations For White-Label Solutions Initial Investment As is the case with any venture, the seed capital required while launching a Web3 project may be high or low. It can also be very expensive to come up with a product from the ground up; this may cost as much as say, $1000 and above. In contrast, a white-label solution is more viable and cheaper because the business does not need to invest a lot of money to develop the application, and it can quickly take it to the market. Maintenance and Upgrades Sustaining and enhancing a Web3 product is not one and done, it costs money. Contract-made solutions sometimes need a team to manage upgrades or to do the patching. This could be really challenging for any non-technical firms and media specifically influencers. However, white-label solutions fix this problem by including maintenance and upgrades as part of the services rendered. Hidden Costs As can be seen, hidden costs can often emerge as a real threat to businesses and companies. These might be charges for extra services, regulatory costs, or even above-market rate hosting costs. These are the possibilities that one should take into account while setting his/her financial plan. Development Timelines While comparing the white label solutions with creating a Web3 product from scratch, one of the most important factors is the time needed to launch the product. Pre-built features are available with white-label solutions which means that you can go live much faster. When developing from scratch, one needs more time coding, testing, and checking for bugs in the system. Market Readiness White-label solutions can be implemented fairly quickly, which is a big plus for any company that is aiming to enter the market for its activities. On the other hand, a custom that is built for Web3 requires much more planning and development which takes time, and thus, products may take time to reach the market. Its findings on technical expertise and resource allocation CCTV have ensured the technical know-how of the personnel and resources have been well directed. Skill Set Requirements The creation of a Web3 product requires a combination of many skills when it is developed from the ground up. While others require special skills in areas such as blockchain, smart contracts as well as decentralized applications (dApps). Also, it is crucial to know about cryptograms and protocols’ security concerning the application. However, white-label solutions are usually already built with more attributes, as a result of which the need for unique skills is eliminated. Outsourcing vs In-House Development Choosing to outsource or to develop internally is the first step to be made. Outsourcing helps to reduce costs and use specialized personnel, but using them means that much control over the project may be lost. Case Study: Playnance UpvsDown White Label Solution Playnance has distinguished itself as one of the leading pioneers of web3 white-label solutions by offering a more accessible model and less costly setup fees, eliminating the financial barrier faced by small businesses. For a significantly lower fee, businesses can implement Playnance's a white-label solution, UpvsDown, with no monthly operating costs. This solution is a Web3 P2P trading game where players predict whether the price of Bitcoin will rise or fall. The smart contract collects fees from the winners each round, and traffic owners (platform, media, or influencer) receive a portion of these fees directly from the winners' pool. This approach empowers traffic owners, influencers and media to seamlessly integrate engaging products like Web3 games into their offerings. By attracting players and generating revenue, these businesses can establish new passive income streams. Conclusion The decision to either build a web3 product from scratch or adopt a white-label solution ultimately depends on a company's preference and material considerations. While both approaches are mutually exclusive, they offer great benefits. In the current age characterized by rapid digital growth, companies are faced with a great dilemma when venturing into Web3 technology: should they go for a white-label solution or develop a product from the ground up? Both have their advantages and disadvantages, and an appropriate selection can make all the difference in the success of a particular enterprise. This article explores both approaches, highlighting their benefits and drawbacks. What Are White Label Solutions? White-label solutions” or “white brands” refer to products and services that are created by one company but sold by another company under a different brand name. This approach enables firms to sell products without having to bear the cost of developing them. The key points about it are that it's ready-made products, customizable branding, and facilities for quick market entry. Advantages of White Label Solutions There are also some benefits of white-label solutions which make most of their tasks easier. More economical: There are no development costs, which is very convenient for non-technical companies, media, or influencers. More economical: There are no development costs, which is very convenient for non-technical companies, media, or influencers. Less time to reach the market: Less time to enter the market. Less time to reach the market: Less time to enter the market. More on brand management: Emphasis is on sales, marketing, and branding. More on brand management: Emphasis is on sales, marketing, and branding. Use Cases of White Label Solutions Use Cases of White Label Solutions Some of these white-label solutions can be used across different sectors: E-commerce: Some retailers don’t sell their products, but rather sell the products of third parties under their brand. E-commerce: Software or web3 dApps: Some companies allow others to rebrand their software or web3 dApps and sell them. Software or web3 dApps: Financial services: They are not banks but sell banking services whereby their clients’ products are branded with the brand of the other financial service providers. Financial services: The Appeal of Building a Web3 Product From Scratch There is absolutely nothing wrong with building a product from scratch. In point of fact, there are some benefits to it including: Customization and Control From the perspective of customization and control, creating your own Web3 product is one of the best options. There is how the product is built and that can be made to the tiniest details. This kind of control makes it possible for you to achieve your objectives as you envision. Market Responsiveness The best way to get a Web3 product is by starting its development from scratch, and it is because of the high chances of creativity that it brings on board. You can use one of the most up-to-date technologies and find new applications that cannot be incorporated into white-label solutions. Long Term Vision Building a web3 product from scratch can go along with developing a long-term vision. This involves planning for future growth. It's critical to create a product that evolves with the market. Crucial Considerations For White-Label Solutions Initial Investment As is the case with any venture, the seed capital required while launching a Web3 project may be high or low. It can also be very expensive to come up with a product from the ground up; this may cost as much as say, $1000 and above. In contrast, a white-label solution is more viable and cheaper because the business does not need to invest a lot of money to develop the application, and it can quickly take it to the market. Maintenance and Upgrades Sustaining and enhancing a Web3 product is not one and done, it costs money. Contract-made solutions sometimes need a team to manage upgrades or to do the patching. This could be really challenging for any non-technical firms and media specifically influencers. However, white-label solutions fix this problem by including maintenance and upgrades as part of the services rendered. Hidden Costs As can be seen, hidden costs can often emerge as a real threat to businesses and companies. These might be charges for extra services, regulatory costs, or even above-market rate hosting costs. These are the possibilities that one should take into account while setting his/her financial plan. Development Timelines While comparing the white label solutions with creating a Web3 product from scratch, one of the most important factors is the time needed to launch the product. Pre-built features are available with white-label solutions which means that you can go live much faster. When developing from scratch, one needs more time coding, testing, and checking for bugs in the system. Market Readiness White-label solutions can be implemented fairly quickly, which is a big plus for any company that is aiming to enter the market for its activities. On the other hand, a custom that is built for Web3 requires much more planning and development which takes time, and thus, products may take time to reach the market. Its findings on technical expertise and resource allocation CCTV have ensured the technical know-how of the personnel and resources have been well directed. Skill Set Requirements The creation of a Web3 product requires a combination of many skills when it is developed from the ground up. While others require special skills in areas such as blockchain, smart contracts as well as decentralized applications (dApps). Also, it is crucial to know about cryptograms and protocols’ security concerning the application. However, white-label solutions are usually already built with more attributes, as a result of which the need for unique skills is eliminated. Outsourcing vs In-House Development Choosing to outsource or to develop internally is the first step to be made. Outsourcing helps to reduce costs and use specialized personnel, but using them means that much control over the project may be lost. Case Study: Playnance UpvsDown White Label Solution Playnance has distinguished itself as one of the leading pioneers of web3 white-label solutions by offering a more accessible model and less costly setup fees, eliminating the financial barrier faced by small businesses. For a significantly lower fee, businesses can implement Playnance's a white-label solution, UpvsDown, with no monthly operating costs. This solution is a Web3 P2P trading game where players predict whether the price of Bitcoin will rise or fall. The smart contract collects fees from the winners each round, and traffic owners (platform, media, or influencer) receive a portion of these fees directly from the winners' pool. This approach empowers traffic owners, influencers and media to seamlessly integrate engaging products like Web3 games into their offerings. By attracting players and generating revenue, these businesses can establish new passive income streams. Conclusion The decision to either build a web3 product from scratch or adopt a white-label solution ultimately depends on a company's preference and material considerations. While both approaches are mutually exclusive, they offer great benefits.

SCRATCH

scratch.fi

-- employees

Since n.d.

COMPANY RANKING

SCRATCH

EVERGREEN INDEX #2588

Representing this company? Click here to claim and customize this page!

Scratch WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

SCRATCH

scratch.fi

-- employees

Since n.d.

COMPANY RANKING

SCRATCH

EVERGREEN INDEX #2588

RELATED COMPANIESRANK

STORIES ABOUT SCRATCH

Representing this company? Click here to claim and customize this page!

Scratch WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!