AI

Poetiq has recently published very interesting results showing that its Meta-System has reached a new state of the art in LiveCodeBench Pro (LCB Pro), a competitive coding benchmark, by automatically building and optimizing its logic harness – without fine-tuning any base model or accessing internal models. Result: GPT 5.5 High with Poetiq’s harness scores 93.9% … Read more

Cline had an ‘agency’ before it was cool, but building on the bleeding edge often leads to some structural debt. Over time, the agent loop and VS Code extension became a package deal—making it a headache to maintain or migrate to a new environment. It is difficult to maintain layering properties in a solid context. … Read more

You are a product of your environment, so choose to have the best. In the age of AI, this adage is as true as the day it was uttered. If you’re going to compete in this fast-paced AI environment with innovation around every corner, being among the industry leaders will do you a lot of … Read more

Understanding the modern architecture of AI harder than before. Open any Hugging Face repository and you’ll usually find large configuration files, layer definitions, parameter calculations, and descriptive model cards. what the model does but it rarely helps you understand How built in. That becomes a problem as many developers end up updating the architecture mentally … Read more

There are some obvious signs that can quickly distinguish between casual and advanced AI users. One, for example, is the use of voice AI in everyday tasks. While most users are still working hard on their keyboard to get the perfect speed, someone with experience in using AI now just talks to it. A well-placed … Read more

In this tutorial, we build a realistic Zero-Trust network simulation by modeling a sub-component environment as a directed graph and enforce every request to gain access through continuous authentication. We use a flexible policy engine that includes ABAC-style permissions and device orientation, MFA, path accessibility, location sensitivity, and live risk signals such as abnormal indicators … Read more

The mouse pointer has been at the heart of the personal computer for more than half a century. Tracks the cursor position. Registers clicks. Moreover, it does nothing. Google DeepMind researchers have revealed a set of test protocols and demos for the most advanced AI-powered pointer: one that understands not just where you’re pointing, but … Read more

class MemoryStoreTool(Tool): name = “memory_store” description = “Save an important fact or piece of information to long-term memory.” def __init__(self, memory: MemoryBackend): self._mem = memory def run(self, text: str, category: str = “general”) -> str: chunk_id = self._mem.store(text, {“category”: category}) return f”Stored as {chunk_id}.” def schema(self) -> Dict: return { “type”: “function”, “function”: { “name”: … Read more

Tilde Research researchers released Auroraa new optimizer for training neural networks that address the structural problem in the widely used Muon accelerator. The error silently kills a significant proportion of MLP neurons during training and keeps them dead forever. Aurora comes with 1.1B parameter pre-test, new state-of-the-art result in modded-nanoGPT speed benchmark, and unlocked codes. … Read more

factor_prices = load_factors_dataset() X_full, F_full = prices_to_returns(prices, factor_prices) X_tr, X_te, F_tr, F_te = train_test_split( X_full, F_full, test_size=0.33, shuffle=False ) fm = MeanRisk( objective_function=ObjectiveFunction.MAXIMIZE_RATIO, risk_measure=RiskMeasure.VARIANCE, prior_estimator=FactorModel(), ) fm.fit(X_tr, F_tr) ptf_fm = fm.predict(X_te); ptf_fm.name = “Factor Model” print(f”nFactor-model Sharpe: {ptf_fm.annualized_sharpe_ratio:.3f}”) pipe = Pipeline([ (“preselect”, SelectKExtremes(k=8, highest=True)), (“optimize”, MeanRisk( objective_function=ObjectiveFunction.MAXIMIZE_RATIO, risk_measure=RiskMeasure.VARIANCE)), ]) pipe.fit(X_train) ptf_pipe = pipe.predict(X_test); ptf_pipe.name = … Read more