March 2026

ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

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While Large Language Models (LLMs) have revolutionized code generation, standard "System 1" approaches, generating solutions in a single forward pass, often hit a performance ceiling when faced with complex algorithmic tasks. Existing iterative refinement strategies attempt to bridge this gap at inference time, yet they predominantly rely on external oracles, execution feedback, or computationally expensive prompt-response cycles. In this work, we propose ReflexiCoder, a novel reinforcement learning (RL) framework that internalizes the structured reasoning trajectory, encompassing initial generation, […]

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From Theory to Practice: Using VAEs for Anomaly Detection

From Theory to Practice: Using VAEs for Anomaly Detection

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Detecting Bearing Failures Before They Happen in Industrial sensors Photo by Maxim Berg on Unsplash Overview This is a companion post to Building Variational Autoencoders (VAEs) From Scratch. In this post, we walk from intuition to implementation of a Variational Autoencoder (VAE) for anomaly detection using an industrial bearing failure data example. We start with the core problem: real machine failures don’t show up as single sensor spikes, but as subtle breakdowns in how sensors relate to each other. From there, […]

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AI Is Not Replacing Software Engineers: It Is Redefining Them

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The New Reality: AI Can Write Code, But Should It Ship Without You? Photo by Daniil Komov on Unsplash There is a lot of talk lately about AI “killing” the software developer role. Headlines claim that junior jobs are vanishing and that manual coding is a dead art. But as someone who has been deeply immersed in the latest AI advancements, my experience lately with AI has been that it isn’t a replacement for the engineer, it’s a powerful, albeit […]

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Everything About P-Values, Significance, and Confidence Intervals in One Sitting

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Why 0.05? The Most Arbitrary Rule in Science That Runs the Entire World Every single day. You check if your Uber actually arrives in the promised 5 minutes. You read 200 Google reviews before picking a restaurant. You notice your phone battery dying at 3 PM even though Apple promised “all-day battery life.” You wonder if that person you’re texting is actually interested or just being polite based on their three-word replies over two weeks. All of that? Hypothesis testing. […]

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Clinejection — Compromising Cline’s Production Releases just by Prompting an Issue Triager

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Clinejection — Compromising Cline’s Production Releases just by Prompting an Issue Triager Adnan Khan describes a devious attack chain against the Cline GitHub repository, which started with a prompt injection attack in the title of an issue opened against the repo. Cline were running AI-powered issue triage using the anthropics/claude-code-action@v1 action, configured to run Claude Code with –allowedTools “Bash,Read,Write,…” any time any user opened an issue in their repo. The configured prompt included the issue title, which meant […]

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Why 2026 is the Year Synthetic Data Becomes Non-Negotiable

Why 2026 is the Year Synthetic Data Becomes Non-Negotiable

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The internet is drying up. Model collapse is real. Privacy law is tightening. Here is what the data tells us and what it means for every AI team building right now. There is a conversation happening in every serious AI lab right now, and it is not about model architecture or compute budgets. It is about data. Specifically, the growing and uncomfortable reality that the supply of high-quality, legally usable, human-generated training data is running out faster than most […]

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Essential Python Libraries for Data Science

Essential Python Libraries for Data Science

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Part 6: Deep Learning and NLP Systems Up to this point in the series, everything we have built has operated on structured, tabular data. We started by establishing numerical and structural foundations with NumPy and Pandas (Part 1). We validated assumptions through visualization and diagnostics ( Part 2). We introduced classical machine learning ( Part 3), extended it into gradient boosting (part 4), and finally explored AutoML ( Part 5)as a controlled acceleration layer. Across all five parts, the same […]

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Essential Python Libraries for Data Science

Essential Python Libraries for Data Science

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Part 5: AutoML and Scalable Experimentation Once data pipelines are stable and models are governed, the bottleneck in data science systems shifts. In the earlier parts of this series, we focused on building control before complexity. We established strong data foundations, validated assumptions through diagnostics, introduced classical machine learning with discipline, and extended into gradient boosting without breaking reproducibility or governance. At that stage, models are no longer fragile. They are reliable. Yet progress often slows. Not because teams lack […]

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FOOM.md — An open research agenda for compression-driven reasoning, diffusion-based context editing, and their combination into a unified agent architecture

FOOM.md — An open research agenda for compression-driven reasoning, diffusion-based context editing, and their combination into a unified agent architecture

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I’ve spent two years developing an open research blueprint for scaling LLM reasoning through compression rather than through longer chains-of-thought. The full document is at foom.md—designed to be read directly or fed into any R&D agentic swarm as a plan. Here’s the summary (which the site or document could really use…) Also quick disclaimer, it is mostly written by AI. Ideas are all my own, but this would take years and years to write and we need to […]

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ProtAlign: Contrastive learning paradigm for Sequence and structure alignment

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Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into consideration. Traditional methods treat sequence and structure separately, limiting the ability to exploit the alignment between the structure and protein sequence embeddings. In this paper, we introduce a sequence structure contrastive alignment framework, which learns a shared embedding space where proteins are represented consistently across modalities. By training on large-scale pairs of sequences […]

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