👋 Hey there, welcome back.
In today’s edition, we will look into
Small language model(SLM)
AI Tools vs. Developer Productivity and more..
📦 AI in Enterprise
ChatGPT Introduced Study Mode: OpenAI introduced Study Mode. It lets users highlight content, ask follow‑up questions and get focused help. Study Mode is available on the free, Plus, Pro and Team plans. Read More
GitHub Copilot debugs UI with Agent Mode and MCP Servers: GitHub Copilot now uses Agent Mode with MCP (Memory, Compute, Persistence) servers. This update helps Copilot understand your codebase, track user‑interface state and debug across files and frameworks Read More
Microsoft introduces GenAIScript: Microsoft released GenAIScript, an open‑source TypeScript library that makes it easier to build generative‑AI apps. It provides a modular way to structure prompts, agents and memory. Read More
Salesforce launched “Agentblazer Legend” AI certification: Salesforce launched the “Agentblazer Legend” AI certification, the highest‑level badge on its Trailhead platform. It helps professionals build autonomous AI agents using Salesforce’s Agentforce system. The program has multiple levels and offers a free first attempt through 2025. Read More
⚒️ Today’s AI Tools
warp.dev: An AI‑powered terminal that generates commands, explains errors, executes tasks with your permission, and understands natural language commands
Sourcegraph Cody: An AI code assistant that uses semantic indexing and intelligent code search across large codebases.
Zed.dev: A new code editor designed for fast collaboration with humans and AI
Aider: An AI pair‑programming tool that runs in your terminal
🧰 Remote AI Job Opportunities
Flasy International - AI Developer
Certa - AI Engineer
Brilo AI - Machine Learning Engineer
📘 AI Learning Resources
Educative.io - Launched 4 AI Agent courses
Udacity - Agentic AI courses
Datacamp: Introduction to AI Agents
🤖 What is a Small Language Model (SLM)?
A small language model (SLM) is an AI system that can understand and generate text like larger models, but is much smaller. Because of its size, it runs on less powerful hardware and can even work offline or on phones.
An interesting thing about SLMs is that they train faster and cost less to deploy.
A recent report predicts the SLM market will grow from $7.66 billion in 2024 to $9.23 billion in 2025 at a compound annual growth rate of 20.4%
Source: The Business Research Company
SLMs can handle tasks like answering questions, writing or summarizing text, translating languages, analyzing sentiment (positive or negative tone), etc.
They do well in specific tasks or domains like customer support, voice assistants, search engines, etc.
SLM Examples: Microsoft’s Phi‑3 Mini, Meta Llama 3, Google’s Gemma, Apple’s OpenELM
The following image outlines the process by which small language models (SLMs) are trained and refined.
Image Source: geeksforgeeks
⚔️ AI Tools Vs Developer Productivity
Key takeaways from the Pragmatic Engineer edition about AI tools in development.
AI Tools Can Slow Down Developers (Initially): Developers using the AI tool Cursor were, on average, 19% slower than those who used no AI at all, despite expecting to be 24% faster.
Learning Curve Affects Productivity: AI-assisted development has a steep learning curve, and productivity may drop during the adjustment phase.
Experienced AI Users Perform Better: One developer with 50+ hours of Cursor experience was 38% faster, showing that familiarity and intentional usage matter.
Context Switching Hurts Productivity: Using AI tools forces context switching while waiting for responses, breaking a developer’s flow state (the zone), and slowing down the work. Source
🧩 Build an AI-Driven Developer Productivity Strategy
AI tools can really help save time and make your work easier, but only if we use them properly.
Save the following image!
1. Find out where you are losing time
Before using any tool, stop and think, where do you feel stuck the most?
Example: Let’s say debugging is what slows you down. It takes you over two hours each debugging. This is where you can try an AI tool with an agent to help speed things up.
2. Pick tools that fit how you work
Do not just use a tool because everyone is talking about it. Choose something that fits into your daily workflow and feels natural to use.
Example: If you use VS Code, try GitHub Copilot or Cursor to help you write common code faster.
3. Start small
You don’t need to change everything at once. Just pick one thing AI can help with and try it.
Example: Start by using Copilot only for writing test cases. Once you get used to it, you can slowly use it for more tasks, like writing functions or spotting bugs.
4. Talk to your team and share what works
If you are part of a team, do not keep your AI wins to yourself. Share what is working and ask others what they have tried too.
Example: Maybe Copilot helped you write Dockerfiles faster. Mention it in your next team meeting and show others how you did it.
5. Keep checking and improving
Use AI for a while, then stop and ask, is this really helping me? is it saving time? if not, try a different tool.
Example: You used an AI tool for writing documentation, but it often gave wrong info. So, you switch to another AI tool, which gives more accurate results based on your code.
📊 AI’s Use Cases - Stats
A diagram from Axify shows that most developers use AI mainly to generate. AI can make work easier by fixing bugs, writing code faster, finding answers quickly, and saving time in reviews.
Source: Axify. So most of the developers are using AI to generate the code
To benefit from AI, choose tools that match your workflow and regularly check whether they are helping you. Know More
🐞 Prompt for Debugging Assistance & Error Resolution
I have a [language] function that’s throwing this error:
Error message: "[include full error]"
My code: [insert code snippet]
Expected behavior: [describe what should happen]
What I have tried already: [list steps]
Please:
- Identify likely causes.
- Suggest fixes or improvements.
- Explain reasoning clearly.