The article critiques the Vatican's new encyclical on AI, arguing that technology is neutral and its impact depends on human users. It highlights the 'miserostat'—humanity's inherent discontent—and warns against the hype and unintended consequences of AI. The author agrees with Pope Leo's concerns about technocratic dominance and coder bias, but insists the real issue is people, not tools.
Technology is not inherently good or evil; it depends on the users.
Human nature has a 'miserostat' that prevents lasting satisfaction from progress.
The Leiden Declaration addresses the growing role of AI in mathematical research, including proof formalization, and raises concerns about reliability, attribution, and publication practices. It offers recommendations for researchers, professional bodies, funders, and policymakers.
Published on 2 June 2026, the Leiden Declaration stems from a 2025 workshop at the Lorentz Center in Leiden.
It covers AI applications in mathematics, such as proof formalization, and concerns about reliability and attribution.
Blue Screen: How Peter Gustafson Defragmented the World, by Kyle Benzle, is a science fiction novel set in 2984, where everything is optimized by technology, exploring the consequences of AI gone awry. The fast-paced story has been compared to Ready Player One and raises questions about AI ethics.
Set in 2984, the world is designed for optimal human living through technology.
Protagonist Peter Gustafson faces unknown threats in an AI-dominated world.
In this article, the author describes the inspiration behind Mythograph Atelier, an AI art studio that creates personalized abstract paintings. The idea combines a museum visit's impact, the vision of dynamic AI-native apps, and the concept of a curious AI that asks questions to understand the user before generating art.
Mythograph Atelier is an AI art studio that creates abstract paintings with personal meaning.
The AI asks questions to understand the user's taste and emotions before generating art.
AI coding tools excel at writing new code but fail during operational incidents like 3am production outages, where knowledge context is missing. Engineers spend 84% of their time on non-coding tasks, primarily context gathering. The article argues for treating team knowledge as infrastructure and suggests capturing rationale, putting constraints in the workflow, and closing the feedback loop from incidents.
AI tools are absent during high-stakes incident response, where knowledge synthesis is critical.
Developers spend only ~16% of time coding; the rest is context search and coordination.
AbTARS is a self-hosted AI agent bridge with persistent memory, self-healing capabilities, and peer-to-peer communication. It connects LLMs to Telegram, Discord, and IRC, running unattended for months with zero idle cost.
Persistent memory with multi-layer recall including emotion tracking and Memory Darwinism.
5-layer supervision stack for unattended operation: heartbeat, process watchdog, external watchdog, OS supervisor, and preventive daily restart.
AgentCrew is a conversation-first, Markdown-first methodology for agentic coding that turns a single chat session into a team process with role assignments, task routing, quality gates, and human approval. It uses a pure-Bash classifier to identify task type and risk level, supports fast and full lanes, and includes safety rules to prevent agents from auto-merging or bypassing review.
Transforms coding agents from single-context to multi-role team workflow
Implemented with Markdown and shell scripts, no daemon required
A satirical guide offering three questionable methods to reduce token usage when coding with AI agents: hijacking command output, playing telephone with code context, and speaking tersely.
Replace verbose command output with a simple 'OK.' using a wrapper script.
Provide only class and function names, letting the AI hallucinate the rest.
Converting PDFs to Markdown can significantly reduce token usage when interacting with LLMs. Tools like UNPDF make this easy, though complex formatting may not convert perfectly.
In this tutorial, we use GEPA as a reflective prompt-evolution framework to improve how a small language model solves multi-step arithmetic word problems. We start from a weak seed prompt, build a deterministic benchmark, and define a structured evaluator that returns actionable feedback. A multi-component setup evolves both the instruction field and the output-format rules together. We then compare the baseline and optimized prompts on a held-out validation set to check whether the gains generalize.
GEPA framework for reflective prompt evolution with structured evaluator feedback
Multi-component prompt optimization evolving instructions and format rules jointly
AI coding tool adoption surges to 90% in 2025, driving project deployment rates from 357 per month in 2021 to nearly 1,000 per month, and past 1,000 by end of 2025. But speed without direction is wasted. Teams must pair high throughput with feedback loops to ensure changes move toward the product ideal. The deployment pipeline must scale with code output or AI investments yield no return.
AI coding tool adoption reaches 90% in 2025; project deployment rates exceed 1,000 per month.
Speed without direction is wasted: use the Bullseye Model to measure product velocity.
This week saw the release of Claude Opus 4.8 with incremental improvements. The Trump Executive Order returned, ushering in a prior restraint era for frontier models. OpenAI released a policy blueprint but also engaged in controversial political activities. The article covers model utility, upgrades, security, deepfakes, and more.
Claude Opus 4.8 offers real improvements over Opus 4.7, becoming the clear daily driver.
Trump's executive order is now in effect, requiring pre-approval for frontier model releases, raising concerns.
Context Mode Insight is an observability platform for enterprise AI engineering, built on an open-source plugin trusted by 250K+ developers. It supports 14 AI assistants, analyzes 222 patterns, and provides role-aware insights via a privacy-first design. The paid tier ($20/seat/month) offers org-level dashboards, REST API, and remote MCP for agents, addressing needs of CTOs, EMs, CISOs, and more.
Context Mode Insight is the first observability layer for AI coding agents, priced at $20 per seat per month.
Built on an open-source plugin with 250K+ developers, supporting 14 AI assistants and 222 patterns.
In June 2026, Deepseek became the top paid software vendor on Ramp's platform as US companies send data directly to the service. Ramp chief economist Ara Kharazian cites cost awareness as a driver but warns about security risks of using Chinese models.
Deepseek ranked first among Ramp's trending software vendors in June 2026.
US companies are turning to Deepseek's paid AI service to reduce costs.