Research on Programmer.ie: Modern AI programming

The Preference Was Only the Beginning

Tue, 07 Jul 2026 00:00:00 +0100

A preference is not only a label on what just happened. When the decision belongs to a continuing trajectory, it can also be evidence about what happens next.

Abstract

Most preference-learning systems stop at the choice.

A model produces two responses. A human selects one. The chosen response becomes positive evidence, the rejected response becomes negative evidence, and the training system moves on.

The work itself usually continues.

The selected answer may later be revised, partially retained, contradicted or abandoned. The rejected alternative may reveal a constraint that remains active long after the immediate decision. The preference is therefore not necessarily the outcome. It may be an event inside a longer trajectory.

The Asset‑Price State: How the U.S. Fiscal Machine Now Depends on Rising Markets

Wed, 17 Jun 2026 17:18:52 +0100

What this post argues

The U.S. does not merely like a high stock market. It increasingly needs one.

The stock market has become an amplifier inside the largest federal revenue pipe: individual income tax. When asset prices rise, capital gains, stock compensation, options, bonuses, business equity, and other asset-sensitive income strengthen federal receipts. When the market falls hard, that same amplifier runs in reverse.

This matters because the real debt problem is not debt alone. It is interest cost relative to federal revenue. If interest grows faster than normal revenue, the system becomes more dependent on asset inflation, AI valuations, national champions, and foreign capital to keep the fiscal machine stable.

Warranted Search: When AI Must Prove Before It Looks

Tue, 02 Jun 2026 18:54:26 +0100

TL;DR

Modern AI systems often retrieve nearby text and generate confident answers, but that is not the same as proof. A citation can be real, the answer can be correct, and the evidence can still fail to support the claim.

This post argues for warranted search: a scoped, claim-driven form of intelligent grep. Instead of asking an AI to rummage through a corpus, we give it a specific claim, a bounded search warrant, and a limited set of safe operations.

AI and the End of Easy Growth

Tue, 19 May 2026 09:52:38 +0100

Constraint, Continuity, and Human Agency

1. Introduction: The Strange Feeling Around AI

Artificial Intelligence is clearly real. The infrastructure buildout is enormous, adoption is accelerating, and AI systems are already reshaping software, research, administration, and knowledge work. Yet for many people, daily life feels strangely unchanged. Housing remains expensive, wages remain under pressure, debt continues to rise, and institutions across the West increasingly appear constrained rather than confident.

This post explores the possibility that AI is currently being deployed less as a technology of broad civilizational expansion and more as a technology of continuity management, a way for highly complex systems under demographic, economic, and energetic pressure to optimize themselves, compress costs, and maintain stability in an era where traditional growth models are becoming harder to sustain.

Canada: When Interest Meets Reliable Revenue

Wed, 15 Apr 2026 12:55:18 +0100

Executive Summary

Canada’s fiscal position looks stable on paper. Headline interest costs consume only ~10.6% of federal revenue. But this ratio masks a structural reality: the engine that drove revenue growth has stalled, and the cost of past debt is rising faster than the system can generate new fiscal space.

For decades, population expansion concealed weak per-capita productivity. In 2025, that demographic engine stopped. At the same time, Canada does not fully capture or retain the economic value it produces, due to commodity pricing discounts, single-customer trade concentration, and high-skill outflows. When these factors are applied to the revenue base, the effective denominator shrinks.

From Fuel Protests to Fiscal Risk: What’s Really Happening in Ireland

Tue, 14 Apr 2026 17:53:37 +0100

Executive Summary

This post applies a simple, testable framework to Ireland’s fiscal system:

Fiscal constraint emerges when the cost of debt rises relative to the revenue supporting it.

In large, stable systems like the United States, this dynamic unfolds gradually. Ireland presents a different case.

While headline metrics suggest strength, three structural factors create a distinct risk profile:

Revenue composition: A significant portion derives from multinational activity and is not fully under domestic control.
Measurement distortion: The effective economic base (GNI*) is ~43% smaller than GDP implies.
Debt repricing: Existing debt is being refinanced at materially higher interest rates.

These factors introduce a critical refinement to the model:

✨ TINY CRITICS: Lightweight Reasoning Checks for Large AI Systems

Sat, 29 Nov 2025 00:06:42 +0000

🥹 0. TL;DR

Large language models write fluent explanations even when they’re wrong. Verifying their reasoning usually requires another LLM slow, expensive, and circular.

We needed something different:

A miniature reasoning critic <50 KB trained on synthetic reasoning mistakes, able to instantly detect broken reasoning in much larger models.

The Tiny Critic:

trains on GSM8K-style reasoning traces generated by DeepSeek or Mistral
uses FrontierLens, and Visual Policy Maps (VPMs) to convert reasoning into canonical numerical features
is just a logistic regression with ~30 parameters
runs in microseconds
plugs into any agent
dramatically improves InitAgent, R1-Loops, and research-planning stability

This post tells the full story how we built it, why it works, and what we learned about the shape of reasoning.

The Space Between Models Has Holes: Mapping the AI Gap

Wed, 22 Oct 2025 20:30:36 +0100

🌌 Summary

What if the most valuable insights in AI evaluation aren’t in model agreements, but in systematic disagreements?

This post reveals that the “gap” between large and small reasoning models contains structured, measurable intelligence about how different architectures reason. We demonstrate how to transform model disagreements from a problem into a solution, using the space between models to make tiny networks behave more like their heavyweight counterparts.

We start by assembling a high-quality corpus (10k–50k conversation turns), score it with a local LLM to create targets, and train both HRM and Tiny models under identical conditions. Then we run fresh documents through both models, collecting not just final scores but rich auxiliary signals (uncertainty, consistency, OOD detection, etc.) and visualize what these signals reveal.

🔦 Phōs: Visualizing How AI Learns and How to Build It Yourself

Thu, 09 Oct 2025 00:30:36 +0100

“The eye sees only what the mind is prepared to comprehend.” Henri Bergson

🔍 We Finally See Learning

For decades, we’ve measured artificial intelligence with numbers loss curves, accuracy scores, reward signals.
We’ve plotted progress, tuned hyperparameters, celebrated benchmarks.

But we’ve never actually seen learning happen.

Not really.

Sure, we’ve visualized attention maps or gradient flows but those are snapshots, proxies, not processes.

What if we could watch understanding emerge not as a number going up, but as a pattern stabilizing across time?
What if reasoning itself left a visible trace?

Detecting AI-Generated Text: Challenges and Solutions

Thu, 13 Mar 2025 13:28:24 +0000

Summary

Artificial Intelligence (AI) has revolutionized the way we generate and consume text. From chatbots crafting customer responses to AI-authored articles, artificial intelligence is reshaping how we create and consume content. As AI-generated text becomes indistinguishable from human writing, distinguishing between the two has never been more critical. Here are some of the reasons it is important to be able to verify the source of information:

Preventing plagiarism
Maintaining academic integrity
Ensuring transparency in content creation
If AI models are repeatedly trained on AI-generated text, their quality may degrade over time.

In this blog post, we’ll explore the current most effective methods for detecting AI-generated text.

Shakespeare and the Bible: An AI Investigation

Tue, 11 Mar 2025 22:47:36 +0000

Summary

Could the greatest playwright of all time have secretly shaped one of the most influential religious texts in history? Some believe William Shakespeare left his mark on the King James Bible hidden in plain sight. With the power of AI, we’ll investigate whether there’s any truth to this conspiracy.

You can read about the conspiracy here: