I used AI with success 5 minutes ago. Just five minutes ago, I was writing a piece of software and relied on AI for assistance. Yet, here I am, starting this blog post by telling you that artificial intelligence, so far, has proven somewhat useless. How can I make such a statement if AI was just so helpful a moment ago? Actually, there's no contradiction here if we clarify exactly what we mean. Here’s the thing: at this very moment, artificial intelligence can support me significantly. If I'm struggling with complicated code or need to understand an advanced scientific paper on math, I can…
Regardless of their flaws, AI systems continue to impress with their ability to replicate certain human skills. Even if imperfect, such systems were a few years ago science fiction. It was not even clear that we were so near to create machines that could understand the human language, write programs, and find bugs in a complex code base: bugs that escaped the code review of a competent programmer. Since LLMs and in general deep models are poorly understood, and even the most prominent experts in the field failed miserably again and again to modulate the expectations (with incredible errors on…
Artificial Intelligence (AI) has long promised to transform software development. Yet, as many experienced engineers discover, initial enthusiasm often settles into a more subdued reality. This is the "Trough of Disillusionment" within the Gartner Hype Cycle—where inflated expectations give way to measured assessments. In this phase, teams confront the practical limitations of AI-driven tools, refine their strategies, and seek a balance between what AI can deliver and what human expertise must still provide. This article continues from AI on the Hype Cycle. We do these things not because they…
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Explore new AI features and AI tools: support for IBM Granite Time Series models and TimesFM models (EA), enhanced Real-Time Context Engine experience, new Agent Skills, Confluent Copilot
Machine learning models increasingly support decisions that carry real consequences. For example, they help fraud analysts identify suspicious transactions, assist doctors in assessing patient risk, and inform hiring decisions that can shape people’s careers.As these models become more complex, explainability has become a cornerstone of trustworthy AI. The idea is straightforward: if people understand why a model reached a particular prediction, they should be able to make better, more informed decisions.But there is a fundamental question that often goes unasked: How do we know whether an…
Use Cases for Generative AI For the past year, almost every week the Software Engineering world is asked about Artificial Intelligence (AI). “What should Capgemini’s AI offerings be?”, “Aren’t you worried about the role of Capgemini software engineers now that AI can write code?” Specifically in these cases, the questions are around Generative AI. To put that in context, here is a recap of some terms used to describe different implementations of AI. Machine Learning A nice dictionary definition of machine learning from Oxford Languages is: “the use and development of computer systems that are…
98% of companies are training AI to act more human, but personality didn't appear anywhere in what customers requested. Other important things did, though.
Learn about the top conversational AI platforms in 2026, including infrastructure, enterprise, open source, and agentic options. See what fits your stack.
Over the past year, the way we use AI at Thumbtack has gone through a few phases. Early on it was mostly curiosity, people experimenting with ChatGPT and Copilot on side projects, sharing tips in Slack. Then the models got noticeably better at working inside real, mature codebases (not just greenfield projects) and the conversation shifted. It stopped being about whether we should adopt AI-assisted development and became about how. Lately, it is moving towards the adoption of end to end agentic workflows for development.I’ve been thinking a lot about what this shift means, not just for our…
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I was curious how well the predictions of the most widely cited AI skeptic I've seen (Ed Zitron) have done, so I looked at how his predictions panned out. To disclose my own biases, I've never had a particularly strong pro or anti AI progress position. For example, in 2022, I did a comprehensive look at predictions Futurists made, including well-respected folks like Kurzweil and found them to be generally wrong on both the prediction results as well as the reasoning. On the flip side, in 2015, I wrote about how people were underestimating AI's ability to displace humans in jobs and have…
null Continue reading The Future of Everything: How Artificial Intelligence is Revolutionizing Education, Traffic Management, Healthcare, and Modern Coding on SitePoint.
I continue to experiment with AI in the context of software engineering. I’m fortunate that my team supports me in exploring different ways to improve our daily work. This week, I designed a team of autonomous agents to implement features, from design to implementation. Why autonomous agents? A long time ago, we were delighted when the IDE offered auto-completion. In the previous two years, things have changed. A lot. Coding assistants have become our primary interfaces for coding.
There've been regular viral stories about ML/AI bias with LLMs and generative AI for the past couple years. One thing I find interesting about discussions of bias is how different the reaction is in the LLM and generative AI case when compared to "classical" bugs in cases where there's a clear bug. In particular, if you look at forums or other discussions with lay people, people frequently deny that a model which produces output that's sort of the opposite of what the user asked for is even a bug. For example, a year ago, an Asian MIT grad student asked Playground AI (PAI) to "Give the girl…
Over the past two years, we’ve witnessed a Cambrian explosion of AI development, with Generative and Agentic AI capturing stakeholders’ attention. AI/ML Engineers are delivering real business value by intermixing LLMs and agentic systems with traditional machine learning systems. It’s been an era of rapid prototyping and quick integrations. Teams have adopted different approaches - each unlocking new potential, but also introducing complexity. As these prototypes evolved into production systems, duplicated effort and inefficiencies began to show. Teams built similar observability and…
It seems like only yesterday we were mocking the AIs for their limits, and tomorrow they're going to be mocking ours. In the meantime, here's a look back at some humorous moments from the last year or so. "Mathing is hard" wrote Timothy W. "I for one welcome our new AI overlords." "AI is replicating" quipped a clever anon who styled themselves AInonymous. "I don't want AI features on my phone. And yet, I'm now getting not just one, but two AI items on my context menu." "AI is for the birds" chirped The Beast in Black. "Looks like someone's AI tool for image classification needs less of the A…
In the current AI discourse, particularly when AI is unreliable or misused, it’s often pointed out that AI is simply a tool. This is a fair way to think about it. Like any tool, there are things it’s good for and things it’s not. Some people are better at using it than others. Some folks […] The post In the Age of AI, You Need Structure appeared first on Atomic Spin.
AI can make the first part remarkably fast. It can find the page, the discussion and the person who might know. The harder work begins when those sources disagree, or when they become stale.
AI-assisted code generation is not free. It comes with a hidden cost: burnout. Are we dangerously ignorant to this problem? And how can we cope with it? In this post, we discuss this question.
Or, when AI tries again. As coders, we try many solutions. Sometimes it's because we're stupid and haven't learned any better yet. Sometimes it's because we're not happy with the result yet. Maybe something gnaws at us. Maybe a thrill to betterment invites us. We care about certain principles related to our craft. We have taste and values. At our best, we seek the true and beautiful. How many times does AI iterate? Well, it's really fast. It can "try" lots of things at the speed of a computer. It's reply to your query isn't its "first" solution. Before you can think, it has "thought" -- many…
Hardly a week goes by without a new foundation model, AI agent, or research assistant promising to transform the way businesses work. Models from Google, OpenAI, Deepseek, Mistral, et al. have made a certain type of artificial intelligence more accessible than ever, and for many tasks they are genuinely impressive. They can summarize documents, answer questions, write reports, generate ideas, and, all in all, help people work faster. Given this context, it's a fair question for customers and prospects to ask: If LLMs I have free access to are so good, why can’t they be used for enterprise…
AI is accelerating software creation and cyberattacks alike. Leaders must secure agents and code at inception, enforce controls at runtime, and validate defenses independently.
* For years, despite functional evidence and scientific hints accumulating, certain AI researchers continued to claim LLMs were stochastic parrots: probabilistic machines that would: 1. NOT have any representation about the meaning of the prompt. 2. NOT have any representation about what they were going to say. In 2025 finally almost everybody stopped saying so. * Chain of thought is now a fundamental way to improve LLM output. But, what is CoT? Why it improves output? I believe it is two things: 1. Sampling in the model representations (that is, a form of internal search). After information…
We are currently suffering from a bizarre industry-wide obsession: prompt engineering. Every week, a new “prompt wizard” posts a cheat sheet claiming that if you frame your request with the right magic incantations “Act as a Senior Principal Architect with 20 years of experience...” the AI will suddenly emit flawless, production-ready software. This is not software engineering. This is spell-casting. The fundamental flaw of the prompt engineering Illusion is simple: natural language is ambiguous, and LLMs (large language models) are non-deterministic black boxes. You can spend hours tweaking…
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizational Second Brain: Building an AI That Learns From Experts appeared first on Engineering at Meta.
Ryan chats with Leo de Moura, Senior Principal Applied Scientist at AWS and the creator of the Lean language, about proving correctness in AI agents with the Lean language, how automated reasoning complements probabilistic AI models, and the use of AI for continuous code optimization.
Last month, I built an AI agent that could search the web before answering a question. While working on the article around that project, I got another project idea and that was around the visibility of a website in the world of AI-powered search. The idea essentially revolves around the fact that whether the AI-powered search mentions your website in its answer or not or whether it cites your website as a source or not, and lastly, compare these resulst with the regular Google search results. Does your website show up when AI-powered search answers questions related to the topics it covers?…
If it’s worth doing by hand, it’s worth automating. Just because not everyone is (yet) a world-class developer; that doesn’t mean we can’t step closer to that expert-level space. In this post, I will introduce AIDE (Artifical Intelligence Development Environment), a powerful workflow that merges AI-driven code generation with a sharp focus on documentation-driven development. With AIDE, I tap into the best of artificial intelligence (AI) while respecting the real human insight needed for domain-specific logic. The result? An environment that streamlines repetitive coding, synchronises…
If it’s worth doing by hand, it’s worth automating. Just because not everyone is (yet) a world-class developer; that doesn’t mean we can’t step closer to that expert-level space. In this post, I will introduce AIDE (Artifical Intelligence Development Environment), a powerful workflow that merges AI-driven code generation with a sharp focus on documentation-driven development. With AIDE, I tap into the best of artificial intelligence (AI) while respecting the real human insight needed for domain-specific logic. The result? An environment that streamlines repetitive coding, synchronises…
The best use of AI I found all year was hiring. This year I used AI to help me fill two roles I had never hired for before: a senior marketing specialist to consult with each of our four regional offices on local marketing needs, and a marketing operations lead to build automations that compress […] The post Spec-Driven Hiring Was My Most Useful AI Win of the Year appeared first on Atomic Spin.
I write a lot, and I use AI a lot. But AI does not write my blog posts. Mostly. AI is useful for making some of my writing less tedious. Recently, I asked GitHub Copilot CLI to read a few dozen posts from this blog and derive a VOICE.md to assist my writing. The result is now checked into this repo and linked from AGENTS.md, so any AI agent working here gets the same reminder: this blog is authored by me, not by AI and the agent’s job is to help me rather than pretend to be me. The useful and interesting part of VOICE.md was how it captured my weirdly specific patterns. Start with a real…
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