RT Mel (@Villgecrazylady)How is it even remotely possible that a university which graduates about 1,800 students each semester with degrees in software engineering needs to look outside our country for a software engineer? ...

🔥 INSIGHT: Software engineer demand has spiked since mid-2025, despite AI advances, per Citadel Securities. https://t.co/0n2Wz42K69 ...

continuing to trace the exponential. I think what will happen is coding will be generally solved for everyone. I think we're going to start to see the title software engineer go away and I think it's just going to be maybe builder, maybe product manager, maybe we'll keep the title as kind of a vestigial thing, but the work that people do, it's not just going to be coding.It's software engineers are also going to be writing specs. they're going to be talking to users like this thing that we're starting to se ...

RT Eric S. Raymond (@esrtweet)If you are a software engineer "experiencing some degree of mental health crisis", now hear this, because I've been coding for 50 years since the days of punched cards and I have a salutary kick in your ass to deliver.Get over yourself. Every previous "programming is obsolete" panic has been a bust, and this one's going to be too.The fundamental problem of mismatch between the intentions in human minds and the specifications that a computer can interpret hasn't gone away just b ...

RT ₕₐₘₚₜₒₙ (@hamptonism)Anthropic CEO:Software engineering will be completely obsolete in 6-12 months… https://t.co/EwKq8l7HE7 ...

RT Haseeb ＞|＜ (@hosseeb)On the one hand, AI influencers are breathlessly raving about Claude Code, Clawdbot, and Cowork. And on the other hand, most people I know—even software engineers—are despondent, overwhelmed about how everything is changing so quickly. I hear this from people early in their careers especially, a fear that everything they've learned and the skills they've gained are rapidly being devalued.This is a mental trap. Don't fall for it. You should not just be watching from the sidelines or r ...

Developer Experience & AI Agents - The software engineering industry has seen rapid changes in the past year, making future predictions difficult [1][2][3] - Companies are questioning whether current investments in developer tools will be valuable in the future [4] - Coding agents are transformative, but not the only investment needed for software engineering organizations [5] - No-regret investments should focus on inputs to AI agents and things around them that enhance their effectiveness [7][8] Development Environment & Tools - Standardize development environments using industry-standard tools to align with AI model training sets [9][10] - Prioritize CLIs or APIs for agent actions to ensure accuracy and effectiveness [13][14] - Validation is crucial; high-quality validation with clear error messages significantly improves agent capabilities [15][16] Codebase & Documentation - Invest in well-structured and testable codebases for better agent performance [18][19] - Comprehensive documentation is essential, especially for information not directly in the code [20][21][22][23][24][25] Code Review & Collaboration - Improve code review velocity to address bottlenecks caused by increased PRs from agentic coding [26][27] - Distribute code review responsibilities and establish clear ownership with SLOs to avoid overburdening individual reviewers [29][30][31] - Maintain high code review quality to prevent a decline in productivity from agentic coders [32][33][34] Key Principle - What benefits humans also benefits AI; investments in these areas will help developers regardless of AI outcomes [44][45]

AI Coding & Productivity - AI is considered a significant technological wave with the potential to create substantial economic impact and productivity gains [3] - The development of effective agentic solutions, particularly in software and coding, has become visible, with widespread adoption of AI assistance in coding since the introduction of GitHub Copilot [4][5] - The industry anticipates that the paradigm of AI assistance will extend to other areas of software development and various industries beyond coding [5] Challenges & Solutions - Generating more AI code without addressing subsequent steps can be a liability, increasing incidents and making code maintenance harder [12][13] - The industry believes the solution lies in applying AI to monitor, maintain, and troubleshoot AI-generated code to improve overall velocity [14][15] - Resolve AI focuses on building AI solutions that prioritize trust for software engineers, allowing AI to investigate and propose solutions, with human oversight before full automation [17][18] Future of AI in Software Engineering - The industry predicts that within a year, AI will become the primary driver of software, with humans overseeing at a higher level, and within two to three years, AI will make most decisions [20] - The industry emphasizes the importance of deep agentic applications that understand the domain and customer context, requiring innovation in models to handle more data and longer task horizons [27] - Multi-agent systems with various layers of guardrails, checks, and validations are crucial for reliable AI performance, with an orchestrator agent managing other agents [31][32][33] AI Model Specialization - The most capable and expensive AI models are typically used at the top level for reasoning and planning, while specialized or open-source models can handle underlying tasks [34][36] - The industry anticipates that domain-specific large models will emerge for areas like software and customer service due to their significant economic impact [36] Adoption & Cultural Impact - While engineers are early adopters of AI, there is some resistance to change and concerns about job security [38][39] - The industry believes the goal is to produce technology faster, benefiting the world, and engineers will operate at a higher level of abstraction, with AI handling low-level tasks [40][44]

Agent Technology Adoption - Agents are increasingly used in software engineering, but deployment results are inconsistent [1] - Agents often perform well in demonstrations but fail in production environments [1] - The issue is not model quality but the readiness of the environment for agents [1] Factors Affecting Agent Performance - Agents require fast feedback loops, clear instructions, and predictable environments [1] - Agents can fail due to missing environment variables, undocumented dependencies, and unwritten rules [1] Agent Readiness Framework - Agent Readiness can be measured and improved to address the challenges [1] - Eight categories determine codebase agent-readiness, including style validation, build systems, development environments, and observability [1] - Organizations can score their repositories, identify quick wins, and create environments where agents can reliably ship code [1] Practical Application - Factory AI's experience running autonomous agents in enterprise production repositories provides real-world insights [1] - A practical framework can help teams make their agents more productive [1]

“Your personality is where your premium is.”Software engineers used to be sought after for their coding abilities, not their bedside manner. That is now changing https://t.co/Sir9lseyEM ...