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Show HN: Solving complex optimization problems with Google OR-Tools in browser
Solve complex optimization models from TypeScript with Google OR-Tools running as multithreaded WebAssembly. Used in PragmaPlanner Run the local test site: npm install npm run dev Install from npm: npm install or-tools-wasm Import the solver API you need from its subpath: import { CpSat } from 'or-tools-wasm/cp-sat'; Public solver APIs live under solver-scoped subpaths: import { CpModel, CpSolver } from 'or-tools-wasm/cp-sat'; import { RoutingIndexManager, RoutingModel } from...
Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition
Announce Type: new Abstract: Large language models for code generation often need to use APIs that are absent from their pretraining data. This requires more than recalling a function name: models must coordinate signatures, module paths, input-output contracts, semantics, and executable usage patterns. Existing novel-API benchmarks are typically static, rely on coarse pass/fail metrics, or use synthetic APIs that may not reflect real library evolution.
Contract2Tool: Learning Preconditions and Effects for Reliable Tool-Augmented LLM Agents
Announce Type: new Abstract: Tool-augmented large language model agents increasingly rely on external APIs, but standard tool schemas describe how to call a tool, not when the tool is causally appropriate or what task state it produces. Causal tool filtering addresses this gap by using lightweight contracts that specify each tool's preconditions, effects, risk level, and cost. However, manually writing and maintaining such contracts does not scale to large or changing tool ecosystems.
NTILC: Neural Tool Invocation via Learned Compression
Announce Type: new Abstract: Agentic tool-calling language models depend on large registries of callable APIs, functions, and local actions. Placing full tool specifications directly in the prompt incurs a cost that scales linearly with the size of the tool registry, rapidly consuming the context budget. As the registry grows, this leads to higher latency and degrades selection accuracy, particularly due to interference from irrelevant tools.
From Custom Logic to APIs: Understanding and Recommending API Replacement Refactorings
Announce Type: new Abstract: Software refactoring is essential for maintaining code quality. However, API replacement refactoring, which replaces custom logic with API calls, remains underexplored. Existing refactoring tools provide limited support for detecting such opportunities because they rely on predefined templates and have difficulty capturing complex, multi-statement semantic equivalents.
Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems
Announce Type: new Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways, and observer-only integrations. A high-risk action such as publishing data externally may therefore appear as a shell command in one runtime, a tool call in another, and a hosted session transition in a third. This makes it difficult to answer a basic governance question consistently: what action was authorized,...
Intel and pals cram 36,864 CPU cores into a 100kW rack while chasing the agentic AI dragon
Intel is working with Foxconn and other infrastructure providers to develop rack-scale reference designs based on the chipmaker’s Xeon processors. Announced during Intel’s Computex keynote on Tuesday, these blueprints aim to provide greater CPU compute densities for running AI agents at scale. While AI models predominantly run on GPUs and other AI accelerators, the agent harnesses, like OpenClaw, which are used to connect them to tools, terminal shells, code interpreters, and other APIs,...
Interpreto: An Explainability Library for Transformers
arXiv:2512.09730v3 Announce Type: replace Abstract: Interpreto is an open-source Python library for interpreting HuggingFace language models, from early BERT variants to LLMs. It provides two complementary families of methods: attribution methods and concept-based explanations. The library bridges recent research and practical tooling by exposing explanation workflows through a unified API for both classification and text generation.
Strava blames zero-code AI apps and scrapers as it tightens API access
The popular fitness-tracking platform, Strava, is restricting access to its API as part of efforts to clamp down on AI scraping, as reported earlier by TechCrunch. Developers who want to build an app using Strava's data now need to pay for a flat $11.99 / month subscription. In an update on its developer hub, Strava blames the change on "zero-code AI tools" that allow users to quickly create apps that "hammer" APIs.
Castor: CERN Advanced STORage Manager
The CERN Advanced STORage manager (CASTOR) is a hierarchical storage (i.e. has disk and tape) management system which was developed at CERN for archiving physics data (with very large data volumes, see the plot on the right). Files can be stored, listed, retrieved and remotely accessed using CASTOR command-line tools or user applications that were developed using the CASTOR API. CASTOR provides a set of access protocols such as XROOT (the main and recommended protocol) and GridFTP.