Vibe coding has significantly accelerated software prototyping but AI
agents frequently recommend insecure configurations, creating security
problems. Gautam Koul, Lucian Moss, Neil Drew-Lopez, and Daberechi
Ruth Edeokoh share their experience while building applications
for Thoughtworks's global marketing. They learned that to combat this we
need to write a security context file to guide the AI, be cautious with AI
permission requests, create a daily secur...
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Stay on top of what’s going on in AI this summer Here at MIT Technology Review, we understand exactly how relentless the pace of news from the world of artificial intelligence…
arXiv:2605.26132v1 Announce Type: new
Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools? We study this setting starting only from unlabeled seed questions with no ground-truth solutions, across three reasoning domains: math, science, and coding. We propose Self-Verified Distillation, a simple post-training refinement algorithm in which the model generates candidate solutions to the...
arXiv:2605.26130v1 Announce Type: new
Abstract: Operational weather prediction at kilometer scales remains computationally prohibitive for traditional numerical weather prediction (NWP) models, limiting forecast access for applications in energy, agriculture, and disaster management that require fine-grained spatiotemporal detail. Here we introduce AirCast-SR, a foundation model for atmospheric super-resolution that downscales global AI weather forecasts from 0.25 degree (~28 km) to 1 km hor...
arXiv:2605.26128v1 Announce Type: new
Abstract: Production LLM systems increasingly require machine-readable outputs: JSON objects, typed traces, regex-constrained fields, and tool-call schemas. This paper targets on-device and low-cost small language model (SLM) deployments, where sub-3B models are attractive for privacy, latency, and commodity hardware but have limited capacity to satisfy schemas while solving tasks. The usual engineering assumption is that hard output constraints improve ...
arXiv:2605.26125v1 Announce Type: new
Abstract: Future missions to Mars and Venus will make use of aerobraking and aerocapture in order to gain mass through the saving of fuel at planetary arrival. So far only aerobraking has been demonstrated, if the Mars Premier project has paved the way for aerocapture, no demonstration was performed due to the project interruption. The use of these techniques induces additional constraints for planetary probes, since additional heating and mechanical loa...
arXiv:2605.26121v1 Announce Type: new
Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws: human taxonomies suffer from ontological misalignment, and Euclidean clustering fails to address embedding anisotropy. We introduce GEM (Geometric Entropy Mixing), a framework reformulating data curation as a variational problem on the hypersphere augmented with a mixing-balance regularizer. By de...
arXiv:2605.26120v1 Announce Type: new
Abstract: Deploying large Transformer-based vision models on resource-limited mobile devices at network edge is severely constrained by hardware limitations and dynamic wireless environments. While federated learning (FL) enables collaborative training without sharing raw data, strictly local fine-tuning of such massive models remains computationally prohibitive for edge devices. Split federated learning (SFL) alleviates this burden by offloading deep la...
arXiv:2605.26119v1 Announce Type: new
Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early demonstrations, but it breaks down when the deployment target is an embedded system with long product lifecycles, vendor-specific kernels, heterogeneous accelerators, safety constraints, and nontrivial I/O paths. In that environment, a model is only one component of a larger execution chain that begi...
arXiv:2605.26118v1 Announce Type: new
Abstract: Porting deep learning algorithms to new hardware accelerators requires developers to repeatedly apply the same low-level optimizations -- quantization, memory access coalescing, tile size tuning, and architecture-specific workarounds -- to every Triton kernel in their code-base. This manual, repetitive effort is a major bottleneck: each kernel demands the same cycle of trial-and-error profiling against hardware constraints that vary across devi...
arXiv:2605.26117v1 Announce Type: new
Abstract: For business process modeling, we can choose between graph-oriented and block-oriented languages. Block-oriented languages are more structured and therefore better understandable for domain experts, while graph-oriented languages allow more modeling freedom and technical versatility for process designers. To bridge this gap between understandability and technical versatility, we propose a participative forward engineering approach. It uses our ...
About PGConf.dev
PGConf.dev is an annual developer event focused entirely on contributing to the PostgreSQL ecosystem, including core software development and community building. It serves as a primary hub for PostgreSQL hackers, maintainers, and ecosystem developers to meet, collaborate, and share knowledge.
This year’s conference was hosted once again in Vancouver, which also happens to be my hometown :D. On top of that, 2026 also marks PostgreSQL’s 30th anniversary, giving this ...
Many performance metrics and system parameters are inherently volatile or fluctuate rapidly. When using a monitoring system that periodically “scrapes” (polls) a target for its current metric value, the collected data point is merely a snapshot of the system’s state at that precise moment. It doesn’t reveal much about what’s actually happening in that area. Sometimes it’s possible to overcome this problem by accumulating those values somehow – for example, by using histograms or...
A 1990s-vintage PostgreSQL extension shipping a critical buffer overflow in 2026 reveals a larger problem: most teams can't even say what's actually installed…
Fourth in a series of dispassionate tours of managed PostgreSQL services. Previously: RDS, Aurora, and Cloud SQL. AlloyDB is Google’s distributed-storage PostgreSQL, the closest architectural parallel on GCP to Aurora on AWS, with enough distinctive differences to be worth understanding as its ow…
LLM Ops
