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I built Talon Audit: a privacy-first approach to website exposure and remediation Most website audit tools answer one narrow question. A performance tool asks whether the page is fast. A security tool asks whether a protection is missing. An accessibility tool asks whether the interface meets specific standards. A conversion tool asks whether users are completing key actions. Visitors do not experience those categories separately. They experience one website. That observation led me to build Talon Audit, a privacy-first website analysis platform that connects technical exposure, reliability, usability, and conversion risk in one workflow. The four-domain model Talon analyses websites across four connected domains: Exposure Public security and privacy signals, including browser protections, TLS, cookies, third-party scripts, mixed content, and visible configuration risks. Integrity Broken resources, failed routes, script errors, redirects, forms, and reliability regressions. Experience Accessibility, mobile usability, visual clarity, speed, and interaction friction. Conversion Messaging, trust signals, pricing paths, calls to action, and barriers that stop users from moving forward. From detection to action The product follows a five-stage workflow: Detect → Explain → Fix → Verify → Monitor Every finding includes: evidence severity confidence impact remediation guidance verification method The goal is not to produce the longest possible report. It is to help teams decide what matters and what to do next. Passive by design Talon performs passive, low-impact analysis only. It does not exploit systems, bypass authentication, or claim to replace authorised penetration testing. Users must confirm they own the website or are authorised to test it. Customer reports are private, and the public demo uses a fictional environment rather than exposing real domains. AI-native, not AI-directed I built Talon through a terminal-first workflow using Codex 5.5 and Claude Fable. AI acc

Shipped this week Workflows are now a first-class Bizbox primitive — PR #86 · v2026.603.0 The biggest drop this week. @DennisDenuto landed Workflows as a company-scoped concept that sits alongside issues and routines — not shoehorned into either. What that means in practice: Google ADK-backed execution — workflow pipelines run as ADK agents, with phase state persisted as run records. Human handoffs baked in — pipelines can pause and wait for a human before resuming. Deliverables that survive — artefacts from each run are persisted and surfaced in the UI. A pipeline graph in the UI — topologically ordered, showing live phase state and console output. This is the foundation. More on what we can build on top of it below. Workflow human-handoffs now route through ClickUp — PR #91 · v2026.605.0 The day after Workflows landed, @angelofallars wired up the last kilometre: when ADK Python code calls input() inside a pipeline, Bizbox now intercepts that call and sends a ClickUp message to collect the human reply — instead of blocking the process forever. A few things that were fixed along the way: input() monkey-patching now works consistently across Python environments (was silently failing in some setups). Failed workflow runs no longer submit deliverables. You only see artefacts from runs that actually completed. ClickUp awaiting-human bridge adapter ships as a pure plugin — PR #78 · v2026.601.0 This one technically crossed the line on the last day of May (23:56 UTC, 31 May), so it's in scope. @ralphbibera ported the ClickUp transport and adapter as a genuine plugin — implementing the AwaitingHumanBridgeAdapter registry interface — without touching bridge core at all. What that gives you: ClickUp works through the same provider-agnostic layer as any future provider (Slack, Discord, whatever comes next). The core doesn't know ClickUp exists. Included: send/poll/reaction transport, message templates for request_confirmation and ask_user_questions interactions, brain_is_think

AutoGen's hidden token tax: why a 3-agent chat costs 15× what you expect Cost-audit series, episode 2. This series began with an AI agent that burned 136M tokens overnight → . AutoGen is Microsoft's multi-agent framework. It's genuinely good at orchestrating agents that hand off work to each other. But its default memory model has a cost shape that surprises almost every team that hits it in production. This audit shows you exactly where the tokens go, with line numbers. The setup: a 3-agent RoundRobin chat The canonical AutoGen pattern is a RoundRobinGroupChat with N agents taking turns on a task. Here's the minimal version from the docs: from autogen_agentchat.agents import AssistantAgent from autogen_agentchat.teams import RoundRobinGroupChat from autogen_agentchat.conditions import MaxMessageTermination planner = AssistantAgent ( " planner " , model_client = client , system_message = " You plan. " ) coder = AssistantAgent ( " coder " , model_client = client , system_message = " You code. " ) reviewer = AssistantAgent ( " reviewer " , model_client = client , system_message = " You review. " ) team = RoundRobinGroupChat ( [ planner , coder , reviewer ], termination_condition = MaxMessageTermination ( max_messages = 10 ), ) await team . run ( task = " Build a web scraper for Hacker News. " ) Three agents, 10 turns total (~3–4 turns each). Seems cheap. It isn't. The default context: unbounded, per-agent Every AssistantAgent gets its own UnboundedChatCompletionContext by default: # autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py, __init__ (L708) if model_context is not None : self . _model_context = model_context else : self . _model_context = UnboundedChatCompletionContext () source UnboundedChatCompletionContext.get_messages() returns self._messages — the full list, no cap, no truncation: # autogen-core/.../model_context/_unbounded_chat_completion_context.py (a ~20-line file) async def get_messages ( self ) -> List [ LLMMessage ]: """ Get at most

After IBM's stock crashed last week on warnings of poor mainframe sales, the CEO explained that AI wrecked corporate hardware budget, temporarily.

We spent the last months consolidating seven separate sequence classifiers into one multi-head model, our apex model, so to speak, and since the weights are now public, I wanted to share what worked and what surprised us. Setup: a shared mmBERT-small encoder with seven task heads, binary injection (BCE), document class (7-way), tool type (14-way), tool operation (6-way), tool data-flow tags (3× BCE, multi-label), intent routing (5-way), and threat type (7-way). The part that needed care: our training rows only carry labels for a subset of tasks, so absent tasks are masked out of the loss entirely. We ended up writing a self-test that asserts absent-task gradients are exactly zero, which caught two subtle bugs, and I'd recommend it to anyone doing similar masking. About 5k synthetic/real multi-task rows help the heads co-train; the test sets stay 100 % real data. Held-out results per head: injection F1 0.962, documents 0.980, tool type 0.957, tool operation 0.945, tool tags 0.958, routing 0.916, threat 0.952. Quantization: both the unified model and the dedicated single-task variants ship quantized -edge builds (ONNX INT8 + INT4 embeddings, from 96 MB) with measured parity benchmarks in the repos, the worst head loses 0.012 against FP32. Was it worth it vs. seven dedicated models? We released both variants, so you can judge for yourself, the dedicated models score marginally higher on most tasks, but the unified one does one encoder pass instead of up to seven. Our weak spot: routing, at 0.916. The intent classes overlap semantically ("write code that analyzes my data" is that code or analytics?), and I suspect the ambiguity is genuinely in the data. If you have ideas beyond relabeling, let me know :) Weights and per-head metrics: https://huggingface.co/patronus-studio submitted by /u/PatronusProtect [link] [留言]

Adding new dimensions to the device.

Less than a week before Meta's lawyers were set to return to a Los Angeles courtroom, the plaintiff accusing the platform of inflicting harm dropped the case. Brought by 15-year-old Florida plaintiff going by initials R.K.C., the case was set to be the second in a set of bellwether trials meant to test legal arguments […]

You can now gift games without a Steam account and sort your wishlist into categories.

Hello. I really liked stackoverflow >5 years ago. There were many people asking about easy-to-medium problems to be solved, and it was a great way for me to learn C/C++/Bash/awk/sed/cmake/Linux/whatever by solving real-life(!) mediocre problems and also helping people in the process and also being criticized and corrected at the same time, from which I learned triple as much. Now stackoverflow is dead. My almost 150k reputation means nothing. Finally they added a "advice" type of questions which is way way too late. Now I lurk over reddit for typic-specific type of questions, but reddit is more a social network then help-me-with-programming-problem site, and the "help me" part is anyway so easy to solve with an AI. It was great back then - the feeling of learning something new and at the same actually helping and actually feeling like an expert in something. I learned the C programming standard by heart by answering really niche questions about C program behaviors. This was really fun for me. I enjoyed the specificity of stackoverflow - the idea of being "exact", answering only the question asked. There are just no questions nowadays on stackoverflow. There is a void now. And AI. There are topic-specific Discord chats, as a modern replacement of IRC, but I always assumed they are used by developers, not for noobs. I know the times without AI will never come again, but the internet, the thing connecting people across the whole globe, just feels more empty, more robotic and like an advertisement nowadays. submitted by /u/kolorcuk [link] [留言]

Most SQL performance tooling requires either a $400/month monitoring agent or asking models and hoping the advice applies to your database. The interesting architecture decisions: Heuristic engine runs first (<200ms), LLM is optional and additive — if LLM fails, you still get structured findings All dialect logic lives in one dialect_config.py — DDL templates, optimizer syntax, LLM system prompts, maintenance commands for all 5 DBs Schema-aware mode: paste DDL, get confirmed recommendations with real table names instead of placeholders Every analysis gets a permanent shareable URL Source: https://github.com/AutoShiftOps/querytuner Live: https://querytuner.com submitted by /u/sajjasudhakararao [link] [留言]

Unity is replacing its scripting runtime. Not tweaking it, replacing it. The Mono runtime that has sat under every line of C# you have written in Unity for years is being retired in favour of Microsoft's CoreCLR. It is the most significant change to Unity's foundation in over a decade, and it is no longer a distant roadmap item: the Unity 6.7 public alpha is out now, with CoreCLR arriving as an experimental option. Most of the coverage treats this as good news wrapped in a version number. It is good news. But if you run a real project, the interesting questions are the practical ones: when does this actually reach me, what changes underneath my game, and what is going to break. Here is that read, from the perspective of a studio that plans and runs Unity upgrades for a living. What CoreCLR Is, and Why Unity Is Doing It Unity has been running a heavily customised fork of Mono for years. That custom fork is the reason Unity has always trailed the wider .NET ecosystem: while the rest of the C# world moved to modern runtimes, garbage collectors, and language features, Unity developers looked on from behind a runtime that could not easily keep pace. CoreCLR is Microsoft's modern, open-source .NET runtime, the same one that powers current .NET. Moving to it does three things at once: it gives Unity a far more capable runtime and garbage collector, it unlocks modern C# and the current .NET library ecosystem, and it dramatically improves iteration time, the write-save-wait-for-domain-reload loop that quietly eats hours of every Unity developer's week. Unity 6.8 is expected to target .NET 10 and C# 14. To make room for it, Unity has done something telling: it paused new work on animation and world-building workflows specifically to concentrate engineering on this migration and on architectural stability. That is a company choosing foundations over features, which is the right call, and a sign of how big this change is. The Timeline That Actually Matters The migration lands a

Unity 6.5 arrived in mid-June 2026, and if you skimmed the announcement you would be forgiven for filing it under "minor update." There is no single headline feature to point at. But 6.5 is more consequential than it looks, because the important changes are subtractions. Several systems that a lot of production projects still lean on have been marked for removal, and the countdown has started. This post is the read we would give a client: what actually changed, which parts matter depending on where you are in your development cycle, and a straight answer on whether to upgrade. First, What Kind of Release This Is Under the Unity 6 model there are two kinds of release, and the difference decides most of the upgrade question on its own. Update releases (6.4, 6.5, 6.6) carry the newest features, platform support, and performance work. Unity describes 6.5 as a Supported release with the same stability and critical-fix quality as an LTS, right up until the next release lands. They are aimed at projects in active or mid-cycle development. LTS releases (6.3 LTS, and 6.7 LTS later this year) are the ones to lock production on. 6.3 LTS is supported with fixes and platform updates through December 2027. They are the safe harbour for a title that is shipping or about to. One date to note if you have not moved recently: Unity 6.0 LTS support ends in October 2026. If you are still on it, that is the real deadline on your calendar, not 6.5. The Real Story: What Is Being Deprecated This is the part worth your attention. None of these break your project today, but each one is a planning item. The Built-In Render Pipeline is deprecated. BIRP still works, and Unity has committed to supporting it through the full 6.7 LTS lifecycle, but it will become obsolete in a future release. If your project is still on BIRP, this is your signal to scope a migration to URP while it is a controlled piece of work rather than something forced on you by an engine upgrade you cannot avoid. Unity has add

If you've ever had to check code or infrastructure against a compliance framework, you know the drill: someone reads a 100-page PDF, then reads your codebase, then makes a judgment call. It's slow, inconsistent, and it can't be automated. So I built a pipeline to fix that — for real. The problem CMMC Level 1 and NIST SP 800-171 Rev 2 are two of the most common compliance frameworks small defense contractors and government-adjacent companies have to meet. Both exist only as dense regulatory text. There's no official machine-readable version. That means every compliance check is manual. Every AI coding assistant reviewing your infrastructure has zero built-in awareness of these requirements. Every CI/CD pipeline has to skip compliance checks entirely or rely on someone remembering to look. ** What I built** A Python pipeline that: Pulls the real regulatory source data — NIST's official CPRT export for SP 800-171, and the verbatim text of 48 CFR § 52.204-21 for CMMC Level 1 Normalizes it into a structured SQLite schema Generates a JSON rule for every single control, with a machine-actionable instruction attached Here's what one rule actually looks like: \ json { "rule_id": "nist_sp_800-171_rev_2_3.1.1", "framework": "NIST SP 800-171 Rev 2", "control_id": "3.1.1", "title": "ACCESS CONTROL — 3.1.1", "requirement": "Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "agent_guidance": "When generating or reviewing code/infrastructure, ensure compliance with NIST SP 800-171 Rev 2 control 3.1.1. Flag any implementation that does not satisfy: Limit system access to authorized users, processes acting on behalf of authorized users, and devices.", "generated_at": "2026-07-15T16:42:56.026218+00:00" } \ \ That agent_guidance field is the interesting part — it's written specifically to drop straight into an AI coding agent's system prompt as a compliance guardrail. Three ways to actually use this 1. AI coding agent system prompt

Java is everywhere in backend systems, enterprise applications, and production environments. But when it comes to numerical computing, data manipulation, and scientific-style operations, Python's NumPy ecosystem has become the standard. I wanted a similar experience in Java: a lightweight, dependency-free library for working with multidimensional arrays and linear algebra. That idea became NumPy4J. What is NumPy4J? NumPy4J is an open-source numerical computing library for Java inspired by NumPy. It provides: Multidimensional arrays (NDArray) NumPy-style broadcasting Array creation utilities Reshaping and slicing Element-wise operations Linear algebra operations Example: NDArray A = NDArray . of ( new double []{ 1 , 2 , 3 , 4 }, 2 , 2 ); NDArray B = NDArray . ones ( 2 , 2 ); NDArray C = A . add ( B ); Matrix operations: NDArray result = LinearAlgebra . matmul ( A , B ); Solving equations: NDArray x = LinearAlgebra.solve(A, b); Why build another numerical library? There are already excellent Java math libraries available. The goal of NumPy4J is different: Provide a NumPy-like API experience Make multidimensional arrays a first-class concept in Java Keep the API simple and approachable Create a foundation for future scientific computing features Testing approach To make sure behavior stays consistent, NumPy4J uses Python NumPy as a reference implementation. Test cases are generated with NumPy and validated against the Java implementation, covering: Broadcasting Matrix operations Reshaping Transpose Linear solving Element-wise calculations What's next? The roadmap includes: More NumPy-compatible operations Matrix decompositions (QR, LU, Cholesky) Eigenvalue computation More statistics functions Performance improvements Try it out If you work with Java and need NumPy-style numerical operations, I would love for you to try NumPy4J, provide feedback, and contribute ideas. GitHub: https://github.com/darius1973/numpy4j Documentation: https://darius1973.github.io/numpy4j/inde

Hey everyone, I'm not in college anymore. Is it possible to do research with a professor or any research lab while working a full time job? If yes, what's the best way to reach out and get involved? Also if anyone looking for someone to work with on a research project or something similar, can dm me. submitted by /u/VastThen1742 [link] [留言]

There’s a debate going on in the Trump administration over how to handle increasingly powerful Chinese AI models.

Treasury Secretary Scott Bessent warned the U.S. government could sanction Chinese AI companies after White House officials accused Moonshot of distilling Anthropic's Fable model to develop Kimi K3.

A closely watched social media addiction lawsuit that had been set to go to trial next week has been dropped after the plaintiff voluntarily dismissed his claims against Meta, leaving none of the major tech companies facing trial in the case.

First four compatible titles can be run with an 11-year-old graphics card.

Two nested loops can do the same work, have the same O(n²) complexity, and still run around 15× apart. A visual explainer on why: the RAM model, the memory wall, cache lines, locality, L1/L2/L3, prefetching, data layout and false sharing. It also covers how the timing difference between a cache hit and miss became the side channel behind Spectre and Meltdown. submitted by /u/Ok_Marionberry8922 [link] [留言]