Ionio

I run Ionio - We are a GPT4 & LLM, AI consulting company. We specialize in training, integrating & deploying LLMs (GPT4, ChatGPT, Alpaca) and other generative AI technologies into existing products and services - enabling up to 20x increase in efficiency and up to 90% operational automation.

Our end-to-end solution covers everything from ideation, development to seamless integration in 6-10 weeks. Value-based billing. 10x ROI Guarantee.

• We built Pumice.ai for Width.ai, a GPT4-powered E-Commerce taxonomy SaaS built with Python - React, Celery, Django, Redis, AWS & Docker in ~10 weeks.

• We helped ClearlyTrained with the strategy, research, ideation & product design of their in-house GPT4-powered eLearning Content solution in 4 weeks. GPT4, Python & NodeJS.

• We built Revvyco.com, an AI-powered revenue optimization platform in for a real-estate consultancy in Utah. Revvy runs on Django, React, Celery, Redis, AWS & Docker and was built in ~16 weeks with a team of 3.

• We built HypeMail.io, an AI-powered cold emailing SaaS built with Python - Flask, React, Celery, Redis, Docker, and AWS in ~12 weeks with a team of 5.

• We built FIT24TW Platform, a fitness startup based in Taiwan - 2 iOS/Android apps & 3 websites based on NodeJS, Flutter, Webflow & Docker in ~12 weeks per entity with a team of 4.

Send me a DM to brainstorm your AI strategy! 🛠

Founder & CEO

SmartCow

• Lead architectural, database, AI/DL and UI/UX design on various Full-stack Applications, built with Python and JavaScript
• Contribute to engineering efforts from planning and organization to execution and delivery to solve complex engineering problems.
• Manage individual projects priorities, deadlines, and deliverables
• Enforcing and maintaining code quality, and limiting technical debt through code reviews.
• Promote a culture of learning within the company, by mentoring, training colleagues, creating learning paths - modular projects which can be used to learn, teach, sharpen skills and then be integrated into production.

Senior Software Engineer

• Architect, develop and optimize the middleware, UI and storage microservices for edge systems.
• Design, build, test, and deploy software for internal and external use
• Ensure the continuous availability and throttling the downtime of core internal frameworks. 
• Design and implement scalable APIs and web services 
• Build dependable test cases and robust CI pipelines for multiple codebases

Software Engineer

Pseudtech Technologies Pvt.Ltd .

• Head research on the entire software side of several pilot-projects.
• Work with Python's leading web frameworks - Django and Flask with Heroku, AWS - S3, RDS and Google Cloud's App Engine to create software products to drive growth
• Work on Computer Vision and Deep Learning projects using OpenCV, Tensorflow, and Keras.
• Design several embedded Linux products including a (Facial Recognition Doorbell) using the Raspberry Pis and Python
• Work with AWS, Google App Engine, Angular and S3 to bootstrap a simple web application to monitor employee productivity
• Creating Facial Recognition POVs utilizing Microsoft Azure's Cognitive Services
• Contacting vendors and suppliers to acquire the resources needed for projects
• Work with Motor Drivers, Servos and Relays to create IOT devices which integrate seamlessly with the company's internal infrastructure. - IOT SwitchBoard
• Creating numerous software POCs and beta projects to meet the requirements.

• Understanding the workflow and commonly faced business problems
• Maintaining existing web apps and routine maintenance.
• Developing small tools and utilities for the upper management to help them automate office tasks.

Junior Software Engineer

Self-Employed

• Developed a Java and JavaFX Desktop App to manage a database of Fixed Deposits of Customers, added SMS integrations, encryption, and FTP Backups

• Created a corporate website for an investment firm using BootStrap, HTML, CSS, configured DNS, CPanel, and optimized it for SEO

• Created a desktop automation software using AutoIT that automated a BPO job of 3 Shifts of unskilled workers working 24x7, saving the client 33% in expenses and increasing efficiency by 50%

• Developed a Python Application to preprocess data received over the network and display it on the screen using PySide and PyQT.

Helping non-tech businesses build software products in 8-12 weeks | DM me your app ideas | Owner - Ionio |

Hi, I'm Rohan! I run Ionio. We are an AI consulting company specialized in LLMs training & deploying, building AI agents & Generative AI. We publish research on our site. Google "Ionio Research"

Our end-to-end solution covers everything from ideation, development to seamless integration in 6-10 weeks.

We are currently doing exactly this for our clients in the SaaS, eLearning, real-estate, fitness & lead generation space. 

Here's what they have to say :)

“We’ve had the opportunity of working with best full stack software engineering company in the biz.  Ionio makes it happen”.
- Greg Warner - Founder - Revvy 🏢

"Working with Ionio has been a game-changer for my business. The way they can take my vision and turn it into a reality better than I imagined is second to none".
- Thomas Manwaring - Founder - HypeMail 📧

“The Ionio team is just fire. Want something done fast? “Yep Yep”. Want something cool and innovative? “We can do that”. Nothing is beyond their reach....”.
- Brooke Daye - Founder - FIT24 🏋🏽‍♂️

Case Studies 👇🏽

• We built a Revvy, a revenue optimization platform in 4 months with a team of 3 engineers for a real-estate consultant in Utah. Property management companies pay a monthly recurring fee to use it. Saved +7.5 hours/week/client, automated 85% of the service

• We built the entire technical infrastructure for FIT24, a fitness startup based in Taiwan - 2 iOS/Android mobile apps & 3 websites in 5 months with a team of 4. Automated away +25 hours/week at the reception desk, saved +40 hours/week total

• We built HypeMail, an email outreach tool for small digital agencies with our client Thomas Manwaring. Took us 5 months & a team of 3. Agencies now pay a monthly recurring fee to use it. Automated away 7.5 hours/week of sales

Drop me a DM if you'd like to brainstorm your AI strategy. 🤖

Previously, I worked as a Software Engineer at SmartCow, an AI company. Before, that I worked in Python for a startup in Mumbai. Before before that for family friends, building websites & apps in Java. Been at this since I was 15 :)

🐺 Back in 2016, I was 16 — had just watched Wolf of Wallstreet. GETTING RICH was my SECOND BIGGEST priority in life. 

Everyone told me, “AI is the FUTURE; it’s where the MONEY is.” 😈

I would stay up late high-school nights, glued to YouTube, Google AI developers’ videos, financial-aid Coursera, black friday Udemy courses & Sentdex videos.

One of the first AI projects where I collected, cleaned the data, trained a model, ran it was a self-driving car in GTA San Andreas…

AI back then was SLOW, HARD, EXPENSIVE, INTIMIDATING — it had a moat.
2024, AI assistants, text-to-image generators, and voice tools are ubiquitous.

Most of these are powered by off-the-shelf APIs wrapped in sleek interfaces, with little to no moat.

Technical implementation of AI has gotten easier, yet senior management is still stuck in 16’. 

"AI strategies need to be reevaluated."

At Ionio, this is exactly the point we try to get across to our clients & prospects.

Here’s what we’ve learned in this journey:

"Why 90% of AI Projects Lack a Competitive Edge in 2024"

🐇 Speed to Market

AI is advancing at an unparalleled pace. Custom models often become outdated within six months due to rapid innovations in open-source alternatives. 

Enterprises struggle to keep up, while companies like Meta release billion-dollar models for free, resetting the playing field.

🦄 Lack of Differentiation

Today’s AI models are mature enough to handle most tasks out of the box. Unless you’re creating something exponentially better and more efficient than open-source alternatives, your efforts may lack substantial value.

🏰 Where’s the Real Moat in AI?

The truth is, the moat isn’t in AI itself—it’s in distribution.

Companies with strong industry expertise and distribution channels are leveraging AI to enhance existing solutions rather than reinvent themselves as AI-first businesses.

Startups often face an uphill battle to build these foundations, while incumbents can integrate AI seamlessly into their verticals.

🧪 What’s the Secret to Successful AI Projects?

The best AI initiatives aren’t marketed as “AI projects.” They’re simply better apps—tools that improve productivity and solve real-world problems.

AI should be a tool, not the end goal. 

Companies that use AI to work faster and smarter without overhauling their core identity are the ones truly winning.

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call. 

Link’s in first comment. ☎️

Rohan Sawant

CEO & Founder | AI, LLM & NLP Consulting | Training, Deploying & Scaling GenAI Applications

😏  "ALL ANALYSTS DO ALL DAY IS LOOK AT YAHOO FINANCE & GOOGLE STUFF," said Pranav Patel rolling his eyes. 👀

He wasn’t COMPLETELY wrong. 

Analysts do spend hours manually piecing together financial reports—digging through historical data, crunching numbers, and running SWOT analyses.

We thought: Why not automate it?

In <10 days, we built a multi-agentic LLM tool (& an agentic library) that generates institutional-grade financial reports in seconds.

✅ Saves 100s of hours/month
✅ Comprehensive SWOT analysis and market positioning
✅ High-level financial metrics and deep technical analysis
✅ Insights into stock performance, historical trends, and more
✅ Cost-effective, scalable, and seamless

We're giving away the code for free. No need to comment "Send" ;)

Here’s how we did it with our proprietary HIVE framework, OpenAI models, and APIs like Yahoo Finance and Perplexity.

🏗️ The Architecture Behind the Magic

HIVE’s multi-agentic framework tackles complex, data-heavy tasks with precision. Here’s how it works:

Master Agent: A large LLM (e.g., OpenAI models) acts as the strategic control center, overseeing and coordinating tasks.

Worker Agents: Specialized smaller models handle niche tasks like data retrieval, SWOT analysis, and number crunching using tools like the Yahoo Finance API and Perplexity.

How It Works in Action for Financial Analysis

1️⃣ Input a stock name (e.g., JP Morgan)

2️⃣ The Master Agent breaks the task into smaller chunks and assigns them to Worker Agents

3️⃣ Worker Agents perform tasks like retrieving historical metrics, running statistical analyses (e.g., T-tests, P-values), and extracting trends

4️⃣ The Master Agent compiles everything into a polished, institutional-grade report

The result? A high-quality financial report featuring market positioning analytics, historical trends, SWOT analysis, and more—all backed by statistical confidence!

Check out our detailed blog post breaking everything down.

Google "Agentic Framework Ionio" for a step-by-step guide & all of the code on setting this up.

Pranav Patel & Shivam Umang Mitter built all of this :)

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call. Link’s in Bio. ☎️

🫨 "OpenAI O1 isn't JUST a SMARTER BETTER LLM!"... it's a paradigm shift in how we think about AI performance. 🤨 

One thing I don't see many people talking about is how it's challenging our fundamental assumptions about speed vs. intelligence in AI. 

While everyone's focused on the benchmark scores, there's a deeper transformation happening in AI that will reshape how businesses deploy these models.

Let me break down what I see coming in the next year...

🔎 1. New Dimensions in LLM Optimization

Until now, improving AI models relied heavily on bigger models, more compute, and extensive training. But with OpenAI o1...

• Organizations will explore existing models using approaches like chain of thought reasoning.

• LLMs will be designed to take longer inference times (think 1–2 minutes) for solving complex tasks instead of relying solely on size.

• This could lead to new workflows and resources for enabling models to "think longer," improving outputs.

🕒 2. Evolving User Expectations

Today, speed is king. Users expect near-instant responses. But that’s changing.

• Slower processing times for more accurate and thoughtful outputs will become acceptable and desirable in many use cases.

• Example: Instead of generating reports in a single pass, teams might use multi-step LLM approaches to ensure depth and precision.

🛠 3. Industry-Specific AI Benchmarks

With reasoning benchmarks like OpenAI o1 pushing boundaries, many traditional benchmarks will become outdated.

• Public benchmarks could leak into training sets or be gamed, losing relevance.

• Enterprises will develop custom benchmarks tailored to their industries, focusing on:
- Multi-step reasoning
- Logical thinking
- Complex problem-solving

This trend will align AI evaluation with real-world enterprise needs, moving beyond older metrics like speed or surface-level benchmarks.

💡 What Does This Mean for You?

1. Be open to slower models—accuracy will increasingly outweigh speed.

2. Invest in reasoning benchmarks that reflect your domain’s unique challenges.

3. Experiment with mental models and multi-step processes to unlock new capabilities in existing LLMs.

At Ionio, over the last 3 years we’ve helped +35 orgs ideate, build & commercialize their AI & SaaS projects. 

This is the ONLY THING we know how to do.

☎️ If you need help with AI in your company, feel free to drop me a DM or book a call. We'd love to help! #ai #genai #llms

🤔 "On a recent call, we made just 3 suggestions to a CTO struggling with their custom LLM deployment. The result? They improved performance 20x to 40x (NOT CLICKBAIT)"🚀

Here's how we did it—and how you can avoid common pitfalls in AI deployments.

🗒️ The Backstory

The client came to us with an MVP built using custom LLMs. They were self-hosting their models on servers in Europe, spending around $500–$700/month on hosting. 

Despite their efforts, they could only process 2000–4000 requests per hour, with GPU utilization stuck at 10–20%.

After DIGGING DEEPER into their setup, here’s what we discovered:

The 3 Key Fixes

1️⃣ Upgrade the Hosting Framework

They were using Ollama, which is great for local development but not for production.

We suggested switching to vLLM, a framework optimized for production deployments.

2️⃣ Optimize GPU Utilization

They ran a single model instance, leaving GPUs severely underutilized.
By running multiple instances of the model with vLLM, they could use their GPUs at 100% capacity, serving far more users simultaneously.

3️⃣ Choose the Right GPU for Inference

They used GPUs that were ONLY optimized for training, not inference.

We recommended switching to Nvidia 3060 GPUs with more VRAM to maximize inference performance and cost efficiency.

🎯 The Results
With these three changes, we helped them:
- Process 20x more requests with the same budget.
- Achieve 500+ requests per minute, even conservatively.
- Potentially hit even higher performance benchmarks with prompt optimization and model quantization.

📝 Takeaway for AI Teams

Even small oversights in hosting setups can lead to massive inefficiencies. With the right tools, deployment strategy, and hardware, you can achieve optimal performance without breaking the bank.

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call. 

Link’s in comments. ☎️

PS Can't believe Pranav Patel actually clicked a photo specifically for this post.

#AI #LLM #MachineLearning #Startups #TechLeadership

“How is building and deploying GenAI projects different compared to Traditional Web Development??” 🤔

We get asked this question a lot of times.

Here's the catch, it's not only different but a completely new approach.

Here’s why: 

Deterministic systems vs Non-Deterministic systems.

🔑 Deterministic Systems- In traditional web development, outputs are predictable. The same code produces the same output.

⚙️ Non-Deterministic Systems- With Generative AI, the output depends heavily on the input. Ask a chatbot about summer fashion trends, and the answers can be dynamic, contextual, and unique every time.

This fundamental shift introduces three major challenges-

💡 1. The Complexity of GenAI Outputs: When building systems like AI-powered retail chatbots, the responses can range from product recommendations and styling advice to customer support.

⚙️ 2. Integrating GenAI With Traditional Systems: The real challenge lies in connecting non-deterministic AI systems to deterministic components like APIs, UIs, and databases. 

We use tools like Pytest and Streamlit UIs to rigorously check every API endpoint and parameter before launch.

🛠️ 3. The Importance of Early Testing: Skipping comprehensive testing during development can lead to catastrophic failures after launch. At Ionio, we’ve learned to prioritize testing from the start—even if it means investing more time upfront.

⚡ Why does this matters??

GenAI isn’t just another add-on. It requires a whole new approach to building, testing, and integrating software.

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call.

Link’s in the bio ☎️

“Hosting a 405B parameter Llama 3 model costs $20–30K/month, while the 70B version is just $5K/month. Yet, in most narrow-use cases, we can match the performance of the larger model.”

Wait what?… Why??? How is this possible? 🤔

The answer lies in model distillation—a technique that allows smaller, more cost-efficient AI models to perform just as well as their larger counterparts.

Here’s how it works:

💡 Model Distillation aka Student-Teacher Paradigm:

In this setup, a larger model (the "Teacher") trains a smaller model (the "Student") by not only providing answers but also explaining its reasoning.

The smaller model learns to replicate the solutions and logic, enabling similar performance at a fraction of the size and cost.

🔍 Real-World Example:

A Llama 3.1 model with just 8 billion parameters (50x smaller than the 405B model) achieves impressive scores on benchmarks like GSM8K and ARC, making it ideal for tasks like summarization and rephrasing.

📉 Cost Savings:

Running a:
- 405B parameter model: $20,000–$30,000/month
- 70B parameter model: $5,000/month
- 8B parameter model: $200–$300/month

By choosing the right model for the right task, companies can reduce expenses by up to 75% or more!

⚡ Speed & Scalability:

Smaller models are faster and more responsive—critical for applications like voice assistants and real-time interactions.

⚠️ Cautionary Tale:

Some may use model distillation to replicate proprietary models via synthetic data—a practice that violates terms of use. 

While cost-effective, it's not recommended for enterprise or public-facing products.

At Ionio, we’ve worked with companies of all sizes to build and launch AI solutions. 

Techniques like model distillation are driving the future of affordable and scalable AI.

As we move into 2025, expect to see smaller, smarter, and faster AI models transforming industries.

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call.

Link’s in the comment. ☎️

😈 Over a cup of tea, a friend who works for a <REDACTED> large online retailor shared a story about their AI project that was meant to “revolutionize customer engagement”. ☕

A platform that would analyze customer interactions across multiple channels, generate personalized responses, and integrate with their legacy CRM system, ticketing platform, and knowledge base. EXCITING!

They had assembled a dream team - ML engineers from top tech companies, experienced backend developers, and business analysts. With a $<REDACTED> M budget, executive buy-in, EVERFTHING. 

The POC using GPT-4 had impressed everyone (OFC IT DID).

Yet six months in, they were drowning in integration challenges. Their legacy CRM used SOAP APIs with complex XML schemas, while their modern ticketing system was REST-based.

The knowledge base was a maze of unstructured data spread across multiple databases. Every time they thought they'd solved one integration issue, three more would surface.

The AI model's responses needed to be validated against compliance rules stored in yet another system. ON & ON & ON….

Systems just REFUSED to play nice together. OFC THEY DID!!.

Found myself nodding vigorously. lol This is all too common.

Here’s a quick breakdown of the real challenges in AI

1️⃣ Disconnected Components, High Complexity

GenAI doesn’t work in isolation. Unlike traditional software, it must integrate with a mishmash of APIs, vendors, and internal systems. This complexity often derails projects from concept to production.

2️⃣ Over-Ambitious Expectations

Senior management envisions AI experiences akin to ChatGPT or MidJourney—tools backed by billions of dollars and years of R&D. Achieving similar results with open-source models and tight budgets in six months? Unrealistic.

3️⃣ Scope Creep & Misaligned Expectations

Unlike traditional SaaS, where new features are visually mapped out, GenAI outputs are “invisible.” This leads to misunderstandings about effort, scope, and feasibility—causing delays and frustration.

4️⃣ Compliance & Control

In regulated industries like finance and healthcare, compliance isn’t just about SOC 2 or GDPR. It’s about ensuring models never produce unauthorized outputs—an uphill battle in GenAI due to inherent unpredictability.

🤔 Why GenAI Projects Are Unique

Building GenAI apps involves aligning diverse teams (business, AI, frontend, backend) and navigating unprecedented challenges, such as prompt injection attacks and unpredictable outputs.

Success requires bridging the gap between technical and business teams, setting clear expectations, and managing complexity strategically.

PS, thumbnail designer (me) should be in actual jail for this one. 🥺 

☎️ If you need help with AI in your company, feel free to drop me a DM or book a call. We'd love to help! #GenAI #AIProjects #EnterpriseAI #Innovation #AIIntegration

🧵 “One RECURRING challenge we’ve tackle at Ionio is optimizing API costs — for our clients we've routinely achieved savings of up to 50%”

Here's a snapshot of how we do it

💸 Understanding the Costs

OpenAI API pricing is influenced by two key factors:
- The model type (e.g., GPT-4 vs. GPT-3.5)
- Token usage—the core focus of our cost-cutting strategies.

🔑 Key Strategies to Slash Costs

🤝 Prompt Caching
By structuring prompts smartly, we leverage OpenAI’s automatic caching for prompts exceeding 1024 tokens.

Benefit: Up to 50% token cost savings and 80% faster response times.

Pro Tip: Place static content at the beginning of prompts and dynamic variables at the end to optimize for caching.

🫥 Predicted Outputs with Speculative Decoding

A cutting-edge feature that uses a smaller model to guess and validate tokens, significantly reducing processing time.

Benefit: Cut response times from 23 seconds to 10 seconds (nearly 60% improvement).

Best for generating text documents and code files.

🤏 Smart Model Selection

Many use cases don’t require the power of GPT-4. Opting for lighter models like GPT-4o-mini or GPT-3.5 can drastically cut costs while delivering high-quality outputs.

Usage Limits and Token Management
- Set strict account limits to control spending.
- Use max token restrictions and adjust temperature settings to reduce unnecessary usage.
- Streamline prompts to avoid redundant instructions or excessive examples.

📊 Results
With a stacked approach using these techniques, 40–50% cost savings are achievable—making your projects not only scalable but also cost-efficient.

You can find our in-depth guide, including code samples, tutorials, and graphs on our blog. Just Google, “Save OpenAI Costs Ionio”

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call. 

Link’s in Bio. ☎️

#GenAI #LLMs #AI

“OpenAI O1 models have 2 CORE INBUILT capabilities that models BEFORE it needed complex LLM frameworks & prompting techniques to achieve.” 🤔

“Chain-of-thought reasoning and adaptive compute.“

These are not just incremental upgrades — they redefine how we work with AI. ⚒️

💡 Chain-of-Thought Reasoning allows the model to break down complex tasks into manageable steps. 

Therefore, instead of manually creating frameworks or workflows, the model handles it internally—asking itself clarifying questions, planning next steps, and building an iterative solution.

⚙️ Adaptive Compute allows the model to allocate extra time and resources to think through particularly complex tasks. 

This ensures better answers, even for highly nuanced tasks.

🔍 Real-World Example:

While drafting a share purchase agreement for a multinational corporate merger with traditional AI, you have to write detailed prompts for every section, go back and forth correcting assumptions and also ensure every nuance is covered.

However, with OpenAI’s advanced models, you’re 80–90% done with this complex documentation. The model outlines the document and breaks it into smaller sections and iteratively drafts and revises the agreement, ensuring that all assumptions and nuances are covered.

🪙 Cost and Speed

O1 models are 10x more expensive than GPT-4, costing $20,000–30,000/month for heavy usage.

Moreover, while GPT-4 is lightning-fast, these models may take 1–2 minutes for complex reasoning.

⚡ Why It’s Worth It:

For tasks requiring deep reasoning and strategy, these models act more like collaborators rather than tools. 

They simplify workflows, save time, and eliminate the need for prompt engineering.

🚨 If you need any other help building AI solutions in your company or if you have any follow questions would be happy to get on a call.

Link’s in the bio. ☎️

⚡ Wrote a quick python script to convert Invoices to JSON data and graphs using LLaMA models.

- Uses Llama-3.2-90B for vision
- Converts them into JSON
- JSON then can be analysed


The fact that these LLMs are dirt cheap now means we don't have to deal with pesky OCR models and various parsers.

More features to add:

- Report Generation: Generate spending reports 
- Deeper Analysis: Analyse and suggest ways to reduce spending.
- Create Balance Sheets and PnL statements: Keep track of spendings and earnings by just uploading images and PDFs.




Access here: 

https://lnkd.in/giEx6Ejj

Blah blah blah so many linkedin words to just say, "I make designs so good, cute girls approach me in coffee shops" 😏

Shinjan P is an engineer, designer at Ionio 

Take notes. 📝

building stuff | sde - ai @ Ionio.ai | gen.ai, saas products ~ "code, coffee, and convincing bugs they're features."

The other day, I was at Starbucks, just chilling with my coffee, when this random girl walks up and starts talking about product development and design.
At first, I thought:
“Okay, this is unexpected.”

But then she said something that really stuck with me:
“Engineers need to understand design. Developers have lost their creativity lately.”

Boom. That hit different.

She wasn’t wrong. Somewhere along the way, we got so focused on writing efficient code and meeting deadlines that we forgot the art of what we do—the creativity, the user experience, the “why” behind every line of code we write.
Her words were a wake-up call. Development isn’t just about making things work—it’s about making them work beautifully. It’s about solving problems creatively, thinking like a designer, and building products that people love to use.

Maybe it’s time we developers brought creativity back into the process. After all, great code is only half the story—design is the other half.
What do you think? Should developers dive deeper into design? Would love to hear your thoughts!

(P.S. The image is blurred for privacy purposes… and because nvm... 😂)

Single large VM with containers for the prod v/s Vercel, Netlify, Amplify, Serverless for an MVP

Founder @ Parlance Labs | Entrepreneur in Residence, ML & Data Science

Don't repeat this mistake.  You have been warned.

I've found that people reach for agent frameworks in a fervor to claim their agent status symbol.  It's very reminiscent of circa 2010 where we saw industries burn billions of dollars blindly pursuing "big data" who didn't need it. 

Don't chase "agents".  Chase solving problems.  Speak in plain language.  Here is a guide I wrote to help companies stave off hype and center themselves on solving problems: https://lnkd.in/gJU8vzDe

Taplio

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