JetBrains vs Replit

How AGI Became a Marketing Department Case

The article discusses how AI, particularly coding agents, is transforming the role of software developers. It highlights that 90% of professional developers use such tools weekly, with 68% daily, and about a third are 'agentic coders' who generate 84% of their code via AI. This shift is changing the developer's role from generalist to specialist, and now to a 'Forward Deployed Engineer' who works closely with clients. The article emphasizes that the real value of developers is shifting from pure coding capacity to understanding business context, identifying problems, and making sound technical decisions. It draws on examples from Google Research and the hiring practices of OpenAI and Anthropic, and suggests that businesses should focus on teams that can quickly build domain context rather than just counting developers.

• 90% of professional developers use coding agents weekly, 68% daily (JetBrains survey).
• One third of developers are 'agentic coders' generating 84% of their code via AI.
• Google Research is investigating whether software agents understand standards and collaborate with developers.

AI Agents Redefine Software Developer Role Toward Context

This opinion piece examines how AI coding agents are reshaping software development, shifting developers' value from implementation to understanding business context and making architectural decisions. JetBrains' survey shows 90% of professional developers use such tools weekly (68% daily), with about a third now 'agentic coders' who let AI generate an average of 84% of their code. This transformation has popularized the 'Forward Deployed Engineer' role at OpenAI and Anthropic, which focuses on client collaboration. The author argues that while AI simplifies coding, experienced developers' true worth lies in contextual understanding, risk identification, and solution validation. The piece advises clients to prioritize contextual insight over mere technical capacity.

• 90% of professional developers use coding agents at least weekly, 68% daily (JetBrains Developer Ecosystem Survey).
• About one-third of developers are 'Agentic Coders', with AI generating 84% of their code on average.
• OpenAI and Anthropic are expanding 'Forward Deployed Engineer' roles, with Anthropic hiring in Munich.

On-Device LLM Chatbot with Kotlin and TensorFlow Lite

This technical tutorial describes how to build an on-device large language model (LLM) chatbot for Android using Kotlin and TensorFlow Lite. It outlines a simple architecture (Chat UI -> ViewModel -> LLM repository -> Tokenizer -> TensorFlow Lite interpreter -> Local model), project setup, model loading, tokenization, background inference with Kotlin coroutines, incremental token handling, conversation-history management, quantization options (FP16, INT8, weight-only) and mobile performance metrics to benchmark (load time, first-token latency, tokens/sec, RAM, battery, thermal). The guide also covers error handling and security considerations (prompts stay on device but APK/model extraction risk), and links to example SDK repos and a Discord community.

• Tutorial outlines an on-device chatbot architecture using a Kotlin Android UI, tokenizer, TensorFlow Lite interpreter, and a local model.
• It shows how to load a .tflite model from app/src/main/assets and initialize a TensorFlow Lite Interpreter in a background runner.
• The guide recommends running inference off the UI thread using Kotlin coroutines (e.g., viewModelScope + Dispatchers.Default) to avoid UI freezes.

Replit Opens First International Office in London

Replit celebrated the opening of its first international office in London, joined by Mayor Sadiq Khan for a ribbon-cutting ceremony, marking the company's first outpost outside the United States.

Replit Opens First International Office in London

Replit celebrated the opening of its first international office in London, joined by Mayor Sadiq Khan for a ribbon-cutting ceremony, marking the company's first outpost outside the United States.

AI Failures Are Human, Not Technical: An Eight-Point Fix

This article argues that most AI project failures are not due to technology but to human and organizational issues. It outlines eight common problems: unclear user intent, mismatched tool selection (agent overuse), unmet user expectations, lack of oversight, insufficient context, imprecise language, missing evaluations, and undefined outcomes. Citing studies and incidents like the Replit database deletion, the author emphasizes the need for better human decisions in AI adoption. The piece provides an actionable checklist for each issue, focusing on intent-based design, appropriate tool usage, setting expectations, implementing least-privilege access, providing rich context, using structured prompts (CARE), establishing evaluation sets, and defining measurable outcomes.

• A 2025 MIT study found roughly 95% of generative AI pilots deliver no measurable impact.
• Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027.
• Gartner estimates only about 130 of thousands of vendors claiming agentic capability are genuine.