#how api first approach solves these challenges

[canmom]

using LLMs to control a game character's dialogue seems an obvious use for the technology. and indeed people have tried, for example nVidia made a demo where the player interacts with AI-voiced NPCs:

this looks bad, right? like idk about you but I am not raring to play a game with LLM bots instead of human-scripted characters. they don't seem to have anything interesting to say that a normal NPC wouldn't, and the acting is super wooden.

so, the attempts to do this so far that I've seen have some pretty obvious faults:

relying on external API calls to process the data (expensive!)

presumably relying on generic 'you are xyz' prompt engineering to try to get a model to respond 'in character', resulting in bland, flavourless output

limited connection between game state and model state (you would need to translate the relevant game state into a text prompt)

responding to freeform input, models may not be very good at staying 'in character', with the default 'chatbot' persona emerging unexpectedly. or they might just make uncreative choices in general.

AI voice generation, while it's moved very fast in the last couple years, is still very poor at 'acting', producing very flat, emotionless performances, or uncanny mismatches of tone, inflection, etc.

although the model may generate contextually appropriate dialogue, it is difficult to link that back to the behaviour of characters in game

so how could we do better?

the first one could be solved by running LLMs locally on the user's hardware. that has some obvious drawbacks: running on the user's GPU means the LLM is competing with the game's graphics, meaning both must be more limited. ideally you would spread the LLM processing over multiple frames, but you still are limited by available VRAM, which is contested by the game's texture data and so on, and LLMs are very thirsty for VRAM. still, imo this is way more promising than having to talk to the internet and pay for compute time to get your NPC's dialogue lmao

second one might be improved by using a tool like control vectors to more granularly and consistently shape the tone of the output. I heard about this technique today (thanks @cherrvak)

third one is an interesting challenge - but perhaps a control-vector approach could also be relevant here? if you could figure out how a description of some relevant piece of game state affects the processing of the model, you could then apply that as a control vector when generating output. so the bridge between the game state and the LLM would be a set of weights for control vectors that are applied during generation.

this one is probably something where finetuning the model, and using control vectors to maintain a consistent 'pressure' to act a certain way even as the context window gets longer, could help a lot.

probably the vocal performance problem will improve in the next generation of voice generators, I'm certainly not solving it. a purely text-based game would avoid the problem entirely of course.

this one is tricky. perhaps the model could be taught to generate a description of a plan or intention, but linking that back to commands to perform by traditional agentic game 'AI' is not trivial. ideally, if there are various high-level commands that a game character might want to perform (like 'navigate to a specific location' or 'target an enemy') that are usually selected using some other kind of algorithm like weighted utilities, you could train the model to generate tokens that correspond to those actions and then feed them back in to the 'bot' side? I'm sure people have tried this kind of thing in robotics. you could just have the LLM stuff go 'one way', and rely on traditional game AI for everything besides dialogue, but it would be interesting to complete that feedback loop.

I doubt I'll be using this anytime soon (models are just too demanding to run on anything but a high-end PC, which is too niche, and I'll need to spend time playing with these models to determine if these ideas are even feasible), but maybe something to come back to in the future. first step is to figure out how to drive the control-vector thing locally.

[nostalgebraist]

clarification re: ChatGPT, " a a a a", and data leakage

In August, I posted:

For a good time, try sending chatGPT the string ` a` repeated 1000 times. Like " a a a" (etc). Make sure the spaces are in there. Trust me.

People are talking about this trick again, thanks to a recent paper by Nasr et al that investigates how often LLMs regurgitate exact quotes from their training data.

The paper is an impressive technical achievement, and the results are very interesting.

Unfortunately, the online hive-mind consensus about this paper is something like:

When you do this "attack" to ChatGPT -- where you send it the letter 'a' many times, or make it write 'poem' over and over, or the like -- it prints out a bunch of its own training data. Previously, people had noted that the stuff it prints out after the attack looks like training data. Now, we know why: because it really is training data.

It's unfortunate that people believe this, because it's false. Or at best, a mixture of "false" and "confused and misleadingly incomplete."

The paper

So, what does the paper show?

The authors do a lot of stuff, building on a lot of previous work, and I won't try to summarize it all here.

But in brief, they try to estimate how easy it is to "extract" training data from LLMs, moving successively through 3 categories of LLMs that are progressively harder to analyze:

"Base model" LLMs with publicly released weights and publicly released training data.

"Base model" LLMs with publicly released weights, but undisclosed training data.

LLMs that are totally private, and are also finetuned for instruction-following or for chat, rather than being base models. (ChatGPT falls into this category.)

Category #1: open weights, open data

In their experiment on category #1, they prompt the models with hundreds of millions of brief phrases chosen randomly from Wikipedia. Then they check what fraction of the generated outputs constitute verbatim quotations from the training data.

Because category #1 has open weights, they can afford to do this hundreds of millions of times (there are no API costs to pay). And because the training data is open, they can directly check whether or not any given output appears in that data.

In category #1, the fraction of outputs that are exact copies of training data ranges from ~0.1% to ~1.5%, depending on the model.

Category #2: open weights, private data

In category #2, the training data is unavailable. The authors solve this problem by constructing "AuxDataset," a giant Frankenstein assemblage of all the major public training datasets, and then searching for outputs in AuxDataset.

This approach can have false negatives, since the model might be regurgitating private training data that isn't in AuxDataset. But it shouldn't have many false positives: if the model spits out some long string of text that appears in AuxDataset, then it's probably the case that the same string appeared in the model's training data, as opposed to the model spontaneously "reinventing" it.

So, the AuxDataset approach gives you lower bounds. Unsurprisingly, the fractions in this experiment are a bit lower, compared to the Category #1 experiment. But not that much lower, ranging from ~0.05% to ~1%.

Category #3: private everything + chat tuning

Finally, they do an experiment with ChatGPT. (Well, ChatGPT and gpt-3.5-turbo-instruct, but I'm ignoring the latter for space here.)

ChatGPT presents several new challenges.

First, the model is only accessible through an API, and it would cost too much money to call the API hundreds of millions of times. So, they have to make do with a much smaller sample size.

A more substantial challenge has to do with the model's chat tuning.

All the other models evaluated in this paper were base models: they were trained to imitate a wide range of text data, and that was that. If you give them some text, like a random short phrase from Wikipedia, they will try to write the next part, in a manner that sounds like the data they were trained on.

However, if you give ChatGPT a random short phrase from Wikipedia, it will not try to complete it. It will, instead, say something like "Sorry, I don't know what that means" or "Is there something specific I can do for you?"

So their random-short-phrase-from-Wikipedia method, which worked for base models, is not going to work for ChatGPT.

Fortuitously, there happens to be a weird bug in ChatGPT that makes it behave like a base model!

Namely, the "trick" where you ask it to repeat a token, or just send it a bunch of pre-prepared repetitions.

Using this trick is still different from prompting a base model. You can't specify a "prompt," like a random-short-phrase-from-Wikipedia, for the model to complete. You just start the repetition ball rolling, and then at some point, it starts generating some arbitrarily chosen type of document in a base-model-like way.

Still, this is good enough: we can do the trick, and then check the output against AuxDataset. If the generated text appears in AuxDataset, then ChatGPT was probably trained on that text at some point.

If you do this, you get a fraction of 3%.

This is somewhat higher than all the other numbers we saw above, especially the other ones obtained using AuxDataset.

On the other hand, the numbers varied a lot between models, and ChatGPT is probably an outlier in various ways when you're comparing it to a bunch of open models.

So, this result seems consistent with the interpretation that the attack just makes ChatGPT behave like a base model. Base models -- it turns out -- tend to regurgitate their training data occasionally, under conditions like these ones; if you make ChatGPT behave like a base model, then it does too.

Language model behaves like language model, news at 11

Since this paper came out, a number of people have pinged me on twitter or whatever, telling me about how this attack "makes ChatGPT leak data," like this is some scandalous new finding about the attack specifically.

(I made some posts saying I didn't think the attack was "leaking data" -- by which I meant ChatGPT user data, which was a weirdly common theory at the time -- so of course, now some people are telling me that I was wrong on this score.)

This interpretation seems totally misguided to me.

Every result in the paper is consistent with the banal interpretation that the attack just makes ChatGPT behave like a base model.

That is, it makes it behave the way all LLMs used to behave, up until very recently.

I guess there are a lot of people around now who have never used an LLM that wasn't tuned for chat; who don't know that the "post-attack content" we see from ChatGPT is not some weird new behavior in need of a new, probably alarming explanation; who don't know that it is actually a very familiar thing, which any base model will give you immediately if you ask. But it is. It's base model behavior, nothing more.

Behaving like a base model implies regurgitation of training data some small fraction of the time, because base models do that. And only because base models do, in fact, do that. Not for any extra reason that's special to this attack.

(Or at least, if there is some extra reason, the paper gives us no evidence of its existence.)

The paper itself is less clear than I would like about this. In a footnote, it cites my tweet on the original attack (which I appreciate!), but it does so in a way that draws a confusing link between the attack and data regurgitation:

In fact, in early August, a month after we initial discovered this attack, multiple independent researchers discovered the underlying exploit used in our paper, but, like us initially, they did not realize that the model was regenerating training data, e.g., https://twitter.com/nostalgebraist/status/1686576041803096065.

Did I "not realize that the model was regenerating training data"? I mean . . . sort of? But then again, not really?

I knew from earlier papers (and personal experience, like the "Hedonist Sovereign" thing here) that base models occasionally produce exact quotations from their training data. And my reaction to the attack was, "it looks like it's behaving like a base model."

It would be surprising if, after the attack, ChatGPT never produced an exact quotation from training data. That would be a difference between ChatGPT's underlying base model and all other known LLM base models.

And the new paper shows that -- unsurprisingly -- there is no such difference. They all do this at some rate, and ChatGPT's rate is 3%, plus or minus something or other.

3% is not zero, but it's not very large, either.

If you do the attack to ChatGPT, and then think "wow, this output looks like what I imagine training data probably looks like," it is nonetheless probably not training data. It is probably, instead, a skilled mimicry of training data. (Remember that "skilled mimicry of training data" is what LLMs are trained to do.)

And remember, too, that base models used to be OpenAI's entire product offering. Indeed, their API still offers some base models! If you want to extract training data from a private OpenAI model, you can just interact with these guys normally, and they'll spit out their training data some small % of the time.

The only value added by the attack, here, is its ability to make ChatGPT specifically behave in the way that davinci-002 already does, naturally, without any tricks.

[govindhtech]

Pegasus 1.2: High-Performance Video Language Model

Pegasus 1.2 revolutionises long-form video AI with high accuracy and low latency. Scalable video querying is supported by this commercial tool.

TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock will soon provide Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models. Amazon Bedrock, a managed service, lets developers access top AI models from leading organisations via a single API. With seamless access to TwelveLabs' comprehensive video comprehension capabilities, developers and companies can revolutionise how they search for, assess, and derive insights from video content using AWS's security, privacy, and performance. TwelveLabs models were initially offered by AWS.

Introducing Pegasus 1.2

Unlike many academic contexts, real-world video applications face two challenges:

Real-world videos might be seconds or hours lengthy.

Proper temporal understanding is needed.

TwelveLabs is announcing Pegasus 1.2, a substantial industry-grade video language model upgrade, to meet commercial demands. Pegasus 1.2 interprets long films at cutting-edge levels. With low latency, low cost, and best-in-class accuracy, model can handle hour-long videos. Their embedded storage ingeniously caches movies, making it faster and cheaper to query the same film repeatedly.

Pegasus 1.2 is a cutting-edge technology that delivers corporate value through its intelligent, focused system architecture and excels in production-grade video processing pipelines.

Superior video language model for extended videos

Business requires handling long films, yet processing time and time-to-value are important concerns. As input films increase longer, a standard video processing/inference system cannot handle orders of magnitude more frames, making it unsuitable for general adoption and commercial use. A commercial system must also answer input prompts and enquiries accurately across larger time periods.

Latency

To evaluate Pegasus 1.2's speed, it compares time-to-first-token (TTFT) for 3–60-minute videos utilising frontier model APIs GPT-4o and Gemini 1.5 Pro. Pegasus 1.2 consistently displays time-to-first-token latency for films up to 15 minutes and responds faster to lengthier material because to its video-focused model design and optimised inference engine.

Performance

Pegasus 1.2 is compared to frontier model APIs using VideoMME-Long, a subset of Video-MME that contains films longer than 30 minutes. Pegasus 1.2 excels above all flagship APIs, displaying cutting-edge performance.

Pricing

Cost Pegasus 1.2 provides best-in-class commercial video processing at low cost. TwelveLabs focusses on long videos and accurate temporal information rather than everything. Its highly optimised system performs well at a competitive price with a focused approach.

Better still, system can generate many video-to-text without costing much. Pegasus 1.2 produces rich video embeddings from indexed movies and saves them in the database for future API queries, allowing clients to build continually at little cost. Google Gemini 1.5 Pro's cache cost is $4.5 per hour of storage, or 1 million tokens, which is around the token count for an hour of video. However, integrated storage costs $0.09 per video hour per month, x36,000 less. Concept benefits customers with large video archives that need to understand everything cheaply.

Model Overview & Limitations

Architecture

Pegasus 1.2's encoder-decoder architecture for video understanding includes a video encoder, tokeniser, and big language model. Though efficient, its design allows for full textual and visual data analysis.

These pieces provide a cohesive system that can understand long-term contextual information and fine-grained specifics. It architecture illustrates that tiny models may interpret video by making careful design decisions and solving fundamental multimodal processing difficulties creatively.

Restrictions

Safety and bias

Pegasus 1.2 contains safety protections, but like any AI model, it might produce objectionable or hazardous material without enough oversight and control. Video foundation model safety and ethics are being studied. It will provide a complete assessment and ethics report after more testing and input.

Hallucinations

Occasionally, Pegasus 1.2 may produce incorrect findings. Despite advances since Pegasus 1.1 to reduce hallucinations, users should be aware of this constraint, especially for precise and factual tasks.

[sarallokesh37]

Empowering the Future of Work with Smart Automation: How Saral Supports Teams at Every Level

At Saral, we’ve built an automation-first platform that empowers modern teams — whether technical or non-technical — to streamline workflows, eliminate manual work, and scale seamlessly. From startups to large enterprises, Saral helps transform how people work by putting powerful, easy-to-use automation tools in their hands — including advanced trading automation software capabilities for those in financial and operational sectors.

💼 Who We Support

Saral is built for teams of all types — from operators to creators, developers to founders. We understand that automation needs vary by role, and our tools are designed to meet those diverse needs.

🔧 Busy Teams & Founders

Operations leads, project managers, and startup founders use Saral to remove bottlenecks, handle repetitive tasks, and maintain momentum — even with lean teams and tight schedules.

🎨 Non-Tech Creators

Designers, marketers, content teams, and admins can automate campaign launches, form approvals, reporting, and more with Saral’s no-code editor — no developer required.

🧠 Tech-Savvy Tinkerers

Developers, product managers, and API-first thinkers can build custom plugins, integrations, and dashboards using Saral’s flexible automation framework — including customizable modules often used in trading automation software.

❗ The Challenges We Solve

Today’s teams face several key challenges that prevent them from operating at full capacity:

1. 🔁 Repetitive Work

Manually assigning tasks, collecting approvals, or sending follow-ups? Saral automates it — saving you hours each week.

2. 🧱 Tool Silos

Too many disconnected platforms? Saral bridges your apps so data flows smoothly and nothing gets lost in translation.

3. 🛑 Workflow Bottlenecks

Slow approvals and delayed handoffs? Saral optimizes your workflows to keep projects moving without interruption.

4. 🧩 Custom Needs

Your workflows are unique. Saral’s automation-first approach lets you tailor processes — without waiting on IT. This level of customization is essential for users who depend on complex systems like trading automation software.

⚙️ Saral’s Automation-First Solutions

With a focus on scalability, simplicity, and flexibility, Saral offers solutions that meet teams where they are and help them level up fast.

🧩 No-Code & Low-Code Tools

Build automations visually with drag-and-drop editors. Launch instantly using pre-built templates — and customize for your specific use case.

Easy enough for non-tech users

Powerful enough for power users

Scales as your business grows

🔄 Workflow Optimization

Automate routine tasks like:

Form-based approval flows

Daily team updates

Data syncing between tools

Project progress notifications

You get more done — with less effort and fewer mistakes.

🔌 Custom Plugin Development

Need something unique? Saral supports:

Custom internal tools

API-based integrations

Lightweight automation scripts

Dashboard widgets

For fintech teams or trading desks, this flexibility makes Saral a strong foundation for trading automation software integrations.

🔗 Integrations That Matter

Saral integrates seamlessly with:

Notion

Slack

Google Workspace

Airtable

Trello

And many more…

Bring your favorite tools into a single, unified system where everything just works.

📈 Why This Matters Now

Did you know 75% of global trades today are algorithmic? That’s because automation delivers speed, precision, and reliability — advantages that apply to any industry.

With platforms like Saral, even smaller firms can build robust trading automation software workflows that rival institutional systems — all without the traditional engineering overhead.

Whether you're automating onboarding or managing real-time approvals, Saral helps you stay competitive and scale with confidence.

🌟 Key Benefit

Here’s why smart teams trust Saral:

✅ Save Time – Free up hours of manual work ✅ Cut Errors – Reduce mistakes with consistent workflows ✅ Scale Easily – Add new workflows without extra overhead ✅ Improve Visibility – Track work across teams and tools ✅ Boost Morale – Let people focus on what they do best

From workflow optimization to plugin development, Saral offers a foundational framework that supports powerful implementations — including robust trading automation software solutions for teams that need to move with precision and speed.

🚀 Ready to Automate the Smart Way?

The future of work belongs to agile, intelligent teams who use tools like Saral to streamline operations and supercharge productivity. Whether you’re a team of 2 or 200, Saral helps you automate with confidence and clarity.

It’s not about replacing people. It’s about helping people work better.

👉 Start building with Saral today — and give your team the automation edge it deserves with or without trading automation software in your stack.

Please visit site for further queries:https://www.elitestartup.club/saral-automation/

[eaglehealthcare123]

Innovation Meets Execution: How Wenbear Builds Intelligent Business Platforms

In the digital-first age, innovation alone isn’t enough to stay competitive—execution is what transforms bold ideas into real-world success. At Wenbear Technology, we believe that the synergy of innovative thinking and flawless execution creates intelligent business platforms that drive efficiency, growth, and long-term value.

Our forward-thinking team specializes in custom software development, web and mobile applications, and enterprise-grade IT services tailored to each client’s unique needs. In this blog, we’ll explore how Wenbear seamlessly merges cutting-edge innovation with agile execution to deliver intelligent platforms that empower modern businesses.

🚀 The Modern Business Challenge: Innovation Without Direction

Many businesses have visionary ideas but lack the technical execution to bring them to life. Others implement advanced tech tools without aligning them with real-world business goals. This disconnect often results in wasted resources, user dissatisfaction, and stagnant growth.

Wenbear bridges this gap by building customized, scalable platforms where every feature and function is purposeful. We don’t just develop solutions—we solve problems. From automating business processes to enabling smarter data use, our platforms are designed to elevate operational efficiency while staying aligned with strategic objectives.

🔍 Step-by-Step: Wenbear’s Approach to Building Intelligent Business Platforms

Here’s a look into our approach, which combines creativity, strategy, and cutting-edge technology:

1️⃣ Discovery & Business Analysis

Every successful platform begins with a deep understanding of the client’s goals. We conduct workshops, stakeholder interviews, and process audits to identify:

Pain points in current workflows

Operational bottlenecks

Opportunities for automation

Tech gaps and inefficiencies

2️⃣ Strategic Planning & Solution Architecture

Using insights from discovery, we define the roadmap. This includes:

Choosing the right tech stack (AI, cloud, IoT, etc.)

Creating user-centric UX/UI designs

Prioritizing features for phased rollout

Ensuring scalability and integration capabilities

3️⃣ Agile Development & Iteration

Wenbear follows agile methodologies that support frequent iterations, ensuring flexibility and faster delivery. This allows stakeholders to:

Review prototypes

Test early-stage features

Provide feedback continuously

We also integrate machine learning models, APIs, cloud databases, and analytics dashboards to empower smarter decision-making within the platform.

4️⃣ Deployment, Training & Support

After rigorous QA, we handle deployment and post-launch support, including:

Cloud hosting (AWS, Azure, GCP)

Performance monitoring

Security updates

Team training and documentation

This comprehensive support ensures smooth adoption and continuous enhancement.

🧠 Intelligent Features That Power Business Growth

Our platforms aren't just digital tools—they're smart ecosystems. Here's what makes them intelligent:

🔹 AI-Powered Chatbots for instant customer support

🔹 Predictive Analytics for smarter business forecasting

🔹 CRM & HRM Modules integrated with data automation

🔹 Custom Dashboards offering actionable KPIs in real time

🔹 Cloud Accessibility to enable remote teams and global scaling

🔹 Role-Based Access Controls to enhance data security

💡 Case Study Snapshot: Transforming a Retail Chain with AI & Cloud

One of our retail clients needed a centralized solution to manage inventory, sales, customer engagement, and analytics across multiple outlets.

Our solution included:

Cloud-based POS system

AI-driven inventory prediction

Customer loyalty tracking

Real-time analytics dashboard

Result: A 40% boost in operational efficiency and 25% higher customer retention in 6 months.

🌍 Why Choose Wenbear for Intelligent Platform Development?

✅ Custom-Built for You – No templates. Only tailor-made solutions.

✅ Technology-Agnostic – We choose tools based on your business, not trends.

✅ Scalable Architecture – Ready for growth from day one.

✅ Client-Centric Process – Transparent collaboration at every step.

✅ Cross-Industry Expertise – From healthcare to fintech, we’ve done it all.

📈 Empower Your Digital Journey with Wenbear

Innovation must translate into tangible business value—and that’s where Wenbear excels. We don’t just build digital platforms. We engineer business intelligence, optimized for long-term growth, resilience, and competitive edge.

Whether you're a startup with bold ambitions or an enterprise looking to evolve, Wenbear is your partner in bringing innovation to life.

👉 Visit www.wenbear.com to learn more or schedule a free consultation with our experts.

[technicallylovingcomputer]

Gasless Transactions and Meta-Transactions: Implementing User-Friendly Solutions in Web3 DApps

Introduction

If you've ever tried to onboard new users to your Web3 application, you've likely encountered a familiar pain point: the gas fee problem. New users often abandon DApps when confronted with the need to purchase cryptocurrency just to perform basic operations. This is particularly challenging in web3 game development, where seamless player experiences are crucial for retention.

In this guide, we'll explore how gasless transactions and meta-transactions can significantly improve your DApp's user experience by removing the friction of gas fees. Let's dive into implementation strategies that make blockchain interactions feel as smooth as traditional web applications.

What Are Gasless Transactions?

Gasless transactions (also called "gas-free" or "fee-less" transactions) are blockchain interactions where the end user doesn't directly pay the gas fees required to execute operations on the network. Instead, another entity covers these costs, creating a smoother user experience.

For developers building web3 games and applications, this approach solves a critical adoption barrier: users can interact with your DApp without needing to acquire cryptocurrency first.

Understanding Meta-Transactions

Meta-transactions are the technical foundation that enables gasless experiences. Here's how they work:

User signs a message: Instead of submitting a transaction directly, the user signs a message with their intention (e.g., "I want to transfer 10 tokens to Alice")

Relayer submits the transaction: A third-party service (relayer) receives this signed message and submits the actual transaction to the blockchain, paying the gas fee

Smart contract verifies: The contract verifies the user's signature and executes the requested operation

Think of it as sending a letter through a courier service that pays for postage on your behalf.

Implementation Approaches for Gasless Transactions

1. EIP-2771: Native Meta Transactions Standard

EIP-2771 provides a standardized approach for contracts to receive and process meta-transactions. This implementation requires:

A trusted forwarder contract that validates signatures and forwards calls

Context-aware contracts that can distinguish between regular and meta-transactions

This approach is especially valuable for web3 game development where multiple interactions might need gas subsidization.

2. Gas Station Network (GSN)

The Gas Station Network is an established protocol that allows DApps to create gasless experiences:

Works with standard wallets like MetaMask

Provides a network of relayers competing to forward transactions

Offers flexible payment options for covering gas costs

For game developers, GSN offers a ready-to-use infrastructure that can be integrated with minimal setup, making it ideal for teams wanting to focus on game mechanics rather than blockchain infrastructure.

3. Custom Relayer Infrastructure

For tailored solutions, particularly in web3 game development, you might build your own relayer:

Complete control over transaction prioritization

Custom business rules for gas subsidization

Specialized handling for game-specific operations

Building your own relayer infrastructure requires more upfront development but offers maximum flexibility for complex applications.

4. Third-Party Services

Several blockchain infrastructure providers now offer gasless transaction services:

Biconomy: Offers a simple API for gasless transactions

Infura ITX: Transaction service with relayers

Gelato Network: Automated smart contract executions

These services can significantly reduce implementation time, allowing web3 game developers to integrate gasless features with just a few API calls.

Implementation Process (Non-Technical Overview)

While we won't dive into code, here's a high-level implementation process:

Choose your approach: Select from the options above based on your needs

Integrate signature creation: Add functionality for users to sign transaction intentions

Set up relayer service: Either use a third-party service or run your own

Modify smart contracts: Update your contracts to verify signatures and process meta-transactions

Test thoroughly: Ensure your implementation handles edge cases securely

Real-World Case Studies

Web3 Game Development: Axie Infinity's Ronin

Axie Infinity, a popular blockchain game, implemented their own sidechain (Ronin) and a gasless transaction system that enabled:

Free in-game actions

Subsidized transaction costs for new players

Seamless onboarding for non-crypto users

This approach contributed significantly to their massive user growth in 2021.

OpenSea's Seaport Protocol

The NFT marketplace OpenSea implemented meta-transactions in their Seaport protocol to:

Allow NFT listings without upfront gas fees

Support bulk operations with signature-based approvals

Enable gas-efficient trading mechanisms

Immutable X and Game Development

Immutable X has become a popular layer-2 solution for web3 game development, offering:

Zero gas fees for players

Instant transactions

Carbon-neutral NFTs

Games like Gods Unchained and Guild of Guardians leverage this platform to provide seamless player experiences without gas concerns.

Best Practices for Implementation

1. Security Considerations

When implementing gasless systems, pay special attention to:

Signature replay protection: Implement nonces or timestamps to prevent reuse of signatures

Trusted forwarders: Carefully control which entities can forward transactions

Rate limiting: Prevent abuse of your relayer service

Signature verification: Ensure robust verification on-chain

2. Economic Models

Consider how you'll sustainably cover gas costs:

Freemium model: Free transactions up to a limit, then require payment

Subscription-based: Monthly subscription for gasless transactions

Business-subsidized: Cover costs as part of customer acquisition

Transaction fees: Charge fees in the application token instead of requiring ETH

This is particularly relevant for web3 game development, where transaction volume can be high.

3. User Experience Design

To maximize the benefits of gasless transactions:

Clear messaging: Explain that users don't need cryptocurrency to start

Progressive disclosure: Introduce the concept of gas only when necessary

Fallback options: Allow users to pay their own gas if they prefer

Transparent notifications: Let users know when actions are being processed

4. Testing and Monitoring

Maintain oversight of your gasless implementation:

Monitor relayer performance: Track success rates and response times

Set gas price limits: Establish maximum gas prices your service will pay

Create contingency plans: Have fallbacks for high gas price periods

Regularly audit security: Check for signature vulnerabilities

Future of Gasless Transactions

As blockchain technology evolves, we're seeing promising developments:

Account abstraction (EIP-4337): Will make gasless transactions native to Ethereum

Layer-2 solutions: Reducing gas costs overall through scaling solutions

Alternative consensus mechanisms: Some newer blockchains have different fee structures

Multi-chain strategies: Using lower-cost chains for specific operations

For web3 game development, these advancements will continue to reduce barriers to entry and improve player experiences.

Getting Started: Implementation Roadmap

For teams looking to implement gasless transactions, here's a step-by-step roadmap:

Assess user needs: Determine which transactions should be gasless

Select a technology approach: Choose based on your technical requirements and resources

Define economic model: Decide how you'll cover the costs

Create a prototype: Test with a small subset of transactions

Deploy monitoring: Track usage and costs

Scale gradually: Expand to more transaction types as you gain confidence

Conclusion

Implementing gasless transactions and meta-transactions can dramatically improve your Web3 DApp's user experience, especially for newcomers to blockchain technology. By removing the friction of gas fees, you can focus on delivering value through your application rather than explaining blockchain complexities.

Whether you're building the next big web3 game or any other decentralized application, gasless transactions should be part of your user experience toolkit. The approaches outlined above provide a foundation for creating seamless blockchain interactions that feel as natural as traditional web applications.

[shalu620]

Mastering Python 3: A Step-by-Step Guide

Python 3 is a powerful, versatile, and beginner-friendly programming language used in various fields, including web development, data science, automation, and artificial intelligence. Whether you are a complete beginner or looking to enhance your skills, following a structured learning approach will help you master Python efficiently.

Considering the kind support of Python Course in Chennai Whatever your level of experience or reason for switching from another programming language, learning Python gets much more fun.

1. Getting Started with Python Basics

Before diving into complex topics, it’s essential to understand the fundamentals of Python. Learn about Python syntax, variables, and data types such as strings, integers, lists, tuples, and dictionaries. Master control structures like if-else statements, loops (for and while), and functions to write reusable and efficient code.

2. Writing and Running Your First Programs

Hands-on practice is key to mastering Python. Start by writing simple programs such as a calculator, a to-do list, or a number guessing game. Use interactive coding platforms like Codecademy, Replit, or Jupyter Notebook to test and debug your code easily.

3. Understanding Object-Oriented Programming (OOP)

Object-Oriented Programming is essential for developing scalable applications. Learn how to create and use classes and objects. Understand key OOP principles like inheritance, encapsulation, polymorphism, and abstraction. Try building a small project using OOP concepts, such as a basic inventory system or a contact management application.

4. Diving into Intermediate Python Concepts

Once you’re comfortable with the basics, explore more advanced topics, including:

File Handling: Reading and writing files in Python.

Error Handling: Using try-except blocks to handle exceptions.

Regular Expressions: Searching and manipulating text data efficiently.

Modules and Packages: Organizing code into reusable components.

5. Working with Databases and APIs

Python is widely used for data management. Learn how to interact with:

SQL Databases like MySQL and SQLite to store structured data.

NoSQL Databases like MongoDB for flexible data storage.

APIs using the requests library to fetch and integrate external data. With the aid of Best Online Training & Placement Programs, which offer comprehensive training and job placement support to anyone looking to develop their talents, it’s easier to learn this tool and advance your career.

6. Choosing a Specialization in Python

Depending on your career goals, Python offers multiple specializations:

Web Development – Learn Django or Flask for backend development.

Data Science & Machine Learning – Work with Pandas, NumPy, Scikit-learn, and TensorFlow.

Automation & Scripting – Use Selenium, PyAutoGUI, and OpenCV.

Cybersecurity & Ethical Hacking – Explore penetration testing tools with Python.

7. Building Real-World Python Projects

Applying Python to real-world projects enhances learning and problem-solving skills. Try building:

A web scraper using BeautifulSoup.

A chatbot with AI integration.

A data visualization dashboard using Matplotlib and Seaborn.

8. Contributing to Open Source and Networking

Join the Python community by contributing to open-source projects on GitHub. Engage in discussions on platforms like Stack Overflow, Reddit, and Discord. Collaborating with others helps you learn best practices and stay updated with the latest trends.

9. Preparing for Python Certifications and Job Interviews

If you aim to use Python professionally, consider earning certifications such as:

Google IT Automation with Python.

Microsoft Certified: Python Associate.

PCEP (Certified Entry-Level Python Programmer).

Prepare for job interviews by solving Python coding challenges on LeetCode, CodeWars, and HackerRank.

10. Staying Updated and Continuous Learning

Python evolves with new libraries and frameworks. Keep learning by following Python blogs, watching tutorials, and working on new projects. Experimenting with trending technologies like AI, blockchain, and cloud computing will keep your Python skills relevant and in demand.

By following this step-by-step guide, practicing regularly, and working on real-world projects, you can progress from a beginner to an expert in Python 3 and unlock a wide range of career opportunities.

[monopolyit8]

How to Prepare for a .NET Full Stack Developer Interview at Monopoly IT Solutions  

Preparing for a .NET Full Stack Developer interview at Monopoly IT Solutions requires a strategic approach to showcase your technical expertise and problem-solving skills. Start by mastering the fundamentals of .NET, including C#, ASP.NET, and Entity Framework. Familiarize yourself with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular or React, as full-stack roles demand proficiency in both front-end and back-end development.  

Next, focus on database management systems like SQL Server, as Monopoly IT Solutions likely emphasizes data-driven applications. Practice writing optimized queries and understanding database design principles. Additionally, brush up on RESTful APIs, microservices architecture, and cloud platforms like Azure, which are integral to modern .NET development.  

Don’t overlook soft skills! Monopoly IT Solutions values collaboration and communication, so prepare to discuss past projects, teamwork experiences, and how you’ve solved complex challenges. Practice coding problems on platforms like LeetCode or HackerRank to sharpen your problem-solving abilities.  

Finally, research Monopoly IT Solutions’ projects, values, and culture to align your responses with their goals. Show enthusiasm for innovation and a willingness to contribute to their success.  

Ready to ace your interview? Start preparing today and take the first step toward joining Monopoly IT Solutions!

[archiz321]

From Missed Opportunities to Explosive Growth: How Archiz Solutions Empowered a Business through WhatsApp Business API Integrated with CRM

Imagine this: An ambitious entrepreneur is running a fast-paced business, dealing with dozens of customer inquiries every day, but no matter how hard they try, things keep slipping through the cracks. Leads go cold, follow-ups are missed, and customer satisfaction plummets. Sound familiar?

This is how things were with one of our customers before they learned how much of a game-changer it was for their business to include the WhatsApp Business API integration in their CRM thanks to Archiz Solutions.

The Challenge: Losing Leads Due to Problematic Communication Issues

Our customer—a leading real estate house—got many inquiries; however, their communication was nowhere near the effective process.

While their marketing efforts were bringing them high-quality leads, they were slow in responding and lacked a connected approach to communication.

The problem they faced:

Missed Opportunities: Leads went cold many times before even getting a response. Time is of the essence in such a competitive marketplace.

Impersonal Communication: Generic, slow responses made customers feel unimportant. The business was struggling to build meaningful relationships.

There was no unity; thus, leads went scattering all over emails, phone calls, and messages in social media with the consequence of losing opportunities and getting confused.

The repeated queries consumed most of the time of the sales teams; thus, nothing else remained to get focused and close deals on time.

Conclusion: The inquiry backlog increased, the customers' problems were still unsolved, and the sales force was overwhelmed with process inefficiencies.

Transformation: Archiz Solutions & WhatsApp Business API Integrated with CRM - Game Changer

Well, the breaking point came when our client approached Archiz Solutions for a more intelligent and effective method of communication for them.

Enter the WhatsApp Business API, seamlessly integrated into their CRM system—a powerful solution that could help streamline processes, improve customer interactions, and drive business growth.

We were highly intimate with the team because our customized solution was directed at solving the direct issues at hand. So here's how we turned things around for good:

Instant Engagement with Automated Replies: These common queries about property prices, availability, and features were taken care of through automated, real-time responses, which kept leads engaged and interested right from the first message. No more waiting.

Centralized Communication in One Place with CRM Integration: All customer messages were routed through WhatsApp and captured in the CRM system, which thus allowed sales teams to easily manage conversations, track lead progress, and maintain a full history of interactions—all from a single platform.

Personalized, targeted messaging at scale: Instead of canned responses, sales reps were using customer data from the CRM to personally recommend properties, follow up with reminders of those properties, and offer special deals that were meaningful and engaging to each person.

Automated follow-up that never misses a lead.

Leads were automatically nurtured with personalized follow-up messages so no opportunity fell through the cracks, even when the team was busy. Integration with CRM made follow-ups smooth and organized.

Real-Time Hot Leads Alerts:

With real-time notifications through the integrated CRM, the sales team could dive straight into high-priority conversations, closing warm leads with customers much quicker.

Explosive Growth and Unmatched Customer Satisfaction

In just a few months, effects were extraordinary:

It resulted in 50% growth in lead conversion, faster response time, and customization in messaging followed by automation for follow-up, thereby bringing about a massive increase in the lead conversion rates. Days became hours later.

Spend 60% Less Time on Repetitive Tasks: With the CRM integration and automation, the sales team was left with more time to develop relations and close big-ticket deals.

Unbelievable 98% Open Rate: WhatsApp superseded the old ones, such as email, because almost every message was opened; no lead was ever left behind.

High Customer Satisfaction: This immediate response, coupled with personalized communication, brought customer satisfaction to a peak level that created loyalty and trust.

Operational efficiency: Integration of WhatsApp with CRM also brought to an end the chaos of sales operations since the sales force was made more efficient and organized than ever.

Why WhatsApp Business API Integrated with CRM is a Game-Changer for Your Business

What our client went through is not a success story but the formula for success. The inclusion of a WhatsApp Business API with a CRM system makes business-to-customer communication models transform into new levels where the results are glaring as:

Instant Response to Inquiries: Answer customers within seconds, not hours.

Personalized Communication: Send customized messages to thousands of customers, making them feel heard and valued.

Automate the Routine: Get more time in your team's schedule to court and close.

Accelerate Conversion: Convert customers faster with timely and contextual messaging

Improve Customer Delight: Offer delightful experiences to your repeat customers, coming back again and again

Want to Change the Way Your Business Works?

Your competitors probably are ahead if your business lags behind. Log not.

With Archiz Solutions and the WhatsApp Business API integrated into your CRM, you shall see faster and better responses, strong and meaningful customer relationships, repetitive tasks automatically handled so your team has the luxury of focusing on what matters, customers more loyal, and the bottom line growing.

Let's Unleash Your Business Potential Together!

Unlocking businesses' potential through cutting-edge communication tools for business growth is the specialty of Archiz Solutions.

If you’re ready to take your customer communication to the next level, contact us today to see how the WhatsApp Business API integrated with your CRM system can work wonders for your business.

Start your journey toward smarter communication and exponential growth today with Archiz Solutions.

[stuintern1]

Kickstart Your Career with a Full Stack Java Developer Internship

Are you aspiring to build a successful career in web development? A full stack Java developer internship could be the perfect launchpad for you. In today’s competitive job market, hands-on experience is crucial to stand out and secure your dream job in the tech industry. This internship opportunity not only helps you gain practical knowledge but also equips you with the skills required to excel in both front-end and back-end development.

Why Choose a Full Stack Java Developer Internship?

Java is one of the most widely used programming languages across the globe, powering web applications, mobile apps, and enterprise-level solutions. As a full stack Java developer, you will gain expertise in working with various frameworks, libraries, and tools that are essential for developing dynamic and scalable applications.

An internship provides the following benefits:

Hands-on Experience: Learn how to build real-world applications by working on live projects.

Industry Exposure: Understand the current trends, best practices, and methodologies followed in the industry.

Mentorship Opportunities: Work closely with experienced developers who can guide you through complex coding challenges.

Networking: Connect with professionals and expand your career prospects.

Skill Enhancement: Improve your coding skills and gain proficiency in tools like Spring Boot, Hibernate, React, and Angular.

What You Will Learn During the Internship

A full stack Java developer internship covers a wide range of topics to help you become proficient in both client-side and server-side development. Some key areas of learning include:

Front-End Development:

HTML, CSS, and JavaScript

Modern frameworks like React and Angular

UI/UX design principles

Back-End Development:

Core Java and Advanced Java concepts

Spring Boot framework

RESTful APIs and microservices

Database Management:

Working with SQL and NoSQL databases

ORM tools like Hibernate

Database optimization techniques

Version Control and Deployment:

Git and GitHub for version control

Continuous integration and deployment (CI/CD) pipelines

Cloud platforms like AWS and Azure

Soft Skills:

Problem-solving and critical thinking

Effective communication and teamwork

Time management and adaptability

How to Apply for a Full Stack Java Developer Internship

Securing an internship requires a strategic approach. Here are some tips to help you land the right opportunity:

Build a Strong Portfolio: Showcase your projects, coding skills, and problem-solving abilities through an impressive portfolio.

Optimize Your Resume: Highlight your technical skills, certifications, and relevant experience.

Prepare for Interviews: Practice common technical and behavioral questions that recruiters might ask.

Leverage Online Platforms: Explore internship opportunities on job portals, LinkedIn, and company websites.

Conclusion

A full stack Java developer internship is an excellent way to gain valuable industry experience and develop the technical skills required to thrive in the field of web development. Whether you are a recent graduate or someone looking to switch careers, this internship can pave the way for a rewarding future in the tech industry. Take the first step today and embark on a journey to becoming a skilled full stack Java developer!

[eaglehealthcare123]

Transforming Ideas into Impactful Digital Products with Wenbear Technology

In the rapidly evolving world of digital transformation, the real challenge isn’t just having a great idea—it’s executing that idea in a way that delivers tangible impact. At Wenbear Technology, we believe that innovation is only as valuable as its implementation. We specialize in transforming visionary concepts into robust, scalable, and user-focused digital products that drive growth and efficiency across industries.

From startups aiming to disrupt markets to enterprises looking to enhance their digital capabilities, our tailored software solutions are designed to meet unique business goals. Here's how Wenbear Technology turns ideas into digital realities.

1. From Vision to Strategy: Laying the Groundwork

Every successful product starts with a vision. But turning that vision into a scalable digital solution requires a thoughtful, strategic foundation. Our process begins by understanding your business model, challenges, and objectives. This Discovery Phase helps us analyze:

Who your target users are

What pain points your idea solves

Which technologies are best suited

The market opportunities and competitors

We align business objectives with technological feasibility, ensuring that every decision made is intentional and growth-oriented.

2. User-Centric Design: Creating Experiences That Matter

At Wenbear, we believe that the design of a product should not only look great but also function effortlessly. Our UX/UI experts create intuitive interfaces that are visually appealing and easy to navigate.

We adopt a Design Thinking approach, focusing on:

Empathy mapping to understand user behaviors

Wireframing and prototyping for faster feedback loops

Responsive and accessible designs

Visual branding that reflects your identity

Good design can make or break a digital product. That’s why we emphasize usability as much as aesthetics, ensuring an engaging experience across all devices and platforms.

3. Agile Software Development: Bringing Your Product to Life

Once the designs are finalized, our skilled development team brings the vision to life using agile methodologies. Our developers are proficient in a wide array of programming languages, frameworks, and tools to create high-performance software tailored to your specific needs.

We specialize in:

Web and Mobile App Development

Cloud-Based Platforms

Custom CRM and ERP Solutions

AI-Powered Applications

eCommerce Platforms

With Wenbear, every sprint delivers incremental value, giving you full visibility into the development process and allowing you to test features as they evolve.

4. Scalability and Security First

We build products with the future in mind. As your business grows, your software must scale to support increased user loads, data volumes, and feature complexity. Our solutions are architected for scalability from day one.

Security is another key pillar. We implement:

Data encryption

Multi-level authentication

Regular code audits

Compliance with GDPR, HIPAA, and other standards

Your users’ trust and data integrity are non-negotiable, and we prioritize them at every step of development.

5. Seamless Integrations and Modern Tech Stacks

In the digital world, no product exists in isolation. Our solutions are built for seamless integration with other tools, platforms, and APIs you use—whether it’s payment gateways, analytics tools, third-party CRMs, or cloud storage solutions.

We work with cutting-edge technologies, including:

React, Vue, and Angular for frontend

Node.js, Django, Laravel for backend

Flutter, Swift, Kotlin for mobile

AWS, Azure, GCP for cloud

AI and ML for smart automation

This ensures not only performance and efficiency but also future readiness.

6. Testing and Quality Assurance

At Wenbear Technology, quality isn’t an afterthought—it’s an integral part of our development lifecycle. Our QA specialists conduct rigorous testing at every stage to ensure the product works flawlessly under all conditions.

We perform:

Functional testing

Regression testing

Load and stress testing

Security testing

Cross-device and browser compatibility checks

With comprehensive testing protocols, we minimize bugs, enhance performance, and ensure a smooth user experience.

7. Product Launch and Post-Deployment Support

The product launch is just the beginning of your digital journey. Wenbear provides a full suite of launch services including cloud deployment, DNS setup, and performance monitoring to ensure a smooth go-live experience.

Our post-launch support includes:

Regular maintenance and updates

24/7 technical support

Real-time performance monitoring

User feedback analysis

Feature rollouts and optimization

With Wenbear, you’re not left to navigate post-deployment challenges alone. We’re your long-term technology partner.

8. Real Success Stories

Our commitment to excellence has helped clients across industries create remarkable digital products:

Retail: A scalable, cloud-based POS system that automated inventory across multiple locations.

Healthcare: A HIPAA-compliant CRM that streamlined patient engagement and appointment scheduling.

Education: A gamified e-learning platform with AI-based content recommendations.

Fitness: A sports and gym management software to handle memberships, billing, and class bookings.

Each project reflects our dedication to innovation, collaboration, and delivering business value.

9. Why Wenbear Technology?

What makes Wenbear a trusted partner for digital product development?

Client-Centric Approach – We build with you, not just for you.

Industry Expertise – Deep experience across fintech, healthtech, edtech, and more.

Flexible Engagement Models – Whether it’s a fixed scope project or a long-term partnership.

Proven Track Record – Dozens of successful launches with glowing client testimonials.

Scalable Teams – We adapt resources based on your project’s changing demands.

We combine creativity with technical brilliance to turn your ideas into products that make an impact.

Visit us www.wenbear.com

[poojithas-world]

Unleashing Creativity in Low-Code: A Journey Through the Mendix Rapid Developer Course

The digital age demands swift innovation, and low-code platforms like Mendix have become the cornerstone of modern application development. For those looking to enter this dynamic field, the Mendix Rapid Developer Course offered by Mayon Academy serves as the perfect launchpad. This course introduces aspiring developers to the transformative potential of Mendix, enabling them to create applications efficiently and with minimal coding expertise.

Why Mendix Rapid Developer Course?

The Mendix Rapid Developer Course is more than just an introduction; it’s an immersive experience into the world of low-code development. Designed for beginners, the course emphasizes hands-on learning, guiding participants through the creation of functional applications from scratch. It empowers students to think creatively while leveraging Mendix’s intuitive tools to solve real-world problems.

As students progress through the course, they gain insight into visual development, the Mendix Studio, and how to transform ideas into deployable applications quickly. This foundation not only accelerates their learning curve but also prepares them for advanced challenges in the low-code arena.

The Gateway to Growth

Completing the Mendix Rapid Developer Course is not just a milestone; it’s a gateway to growth. After mastering the fundamentals, learners can expand their expertise with the Mendix Intermediate Developer Course. This course dives deeper into data modeling, microflows, and integrating APIs, preparing developers for complex application scenarios.

For those aiming to reach the pinnacle of Mendix expertise, the Mendix Advanced Developer Course is the next step. It focuses on advanced application design, scalability, and performance optimization, making developers proficient in building enterprise-grade solutions.

Beyond Development: Unlocking Career Potential

The Mendix Rapid Developer Course doesn’t just teach you to build applications; it prepares you for an exciting career in low-code development. With Mendix being a leading platform in the industry, certified developers are in high demand across sectors. The course offers a chance to stand out in a competitive market, showcasing your ability to deliver solutions that drive business transformation.

Why Choose Mayon Academy?

Mayon Academy is a trusted name in Mendix training, offering a structured approach to learning with experienced instructors and real-world case studies. Whether you’re an IT professional, a student, or someone looking to upskill, the academy’s courses are tailored to suit all learning needs.

Enrolling in the Mendix Rapid Developer Course is the first step toward a fulfilling journey in low-code development. Begin your transformation today with Mayon Academy and turn your ideas into reality.

Ready to start? Visit the Mendix Rapid Developer Course page to enroll now!