Sunday comics for 2026-10-25.

Right-wing provocateurs James O'Keefe and Cam Higby are facing election-related charges after posting videos intended to expose alleged election security deficiencies.

Trump administration officials reneged on the U.S. Department of State's agreement to pay an Alabama-based construction giant for cost overruns while it continued working on a dozen embassy projects during the COVID-19 pandemic, according to the company's Wednesday lawsuit that seeks at least $461 million in damages.

Communities of Light: Standing Together Against Domestic Violence Township of Hopewell

This tiny clip-on camera is worth a look if you want to capture quick photos or video without pulling out your phone, whether that's for kids' activities, outdoor trips, or just documenting your day hands-free. The included 32GB card means you can start shooting right away without buying separate storage. Buy Now at Newegg Features 1080p HD video recording Compact pocket-sized design Includes 32GB SD card Y2K retro aesthetic Portable for travel and vlogging

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine What I Built request was the HTTP library of Node.js for a decade. In 2019 its author wrote #3142, "Request's Past, Present and Future", and in 2020 it went into maintenance mode. The code is still on npm. The reasons are scattered across 3,477 issues and pull requests, 12,441 comments and 2,290 commits, and the people who knew them have moved on. git séance lets you ask that history a question. A local open-weight model (Gemma 4 through Ollama) answers as the project's ghost, but it may only speak from the history: every claim cites an issue ([#3142]) or a commit ([b66e4cd]), and the code removes any citation that isn't in what was actually retrieved. It's for anyone who inherits or depends on an abandoned project and wants to know why it is the way it is, before they "fix" something that was a deliberate decision ten years ago. Demo ( ( ) | .-'-. | | '---' You light a candle for request/request (2011-2026). 3477 threads, 12441 comments and 2290 commits stir in the dark. Ask, and they will answer. you › How did request get automatic gzip decompression of responses? The spirits whisper that the automatic decompression began with a dedicated effort to "Add support for gzip content decoding" [#951]. This feature was implemented via a pull request, merging into the project history with the commit [b66e4cd]. The goal was to allow the library to "Decompress 'x-gzip' content-encoding responses when {gzip: true}" [#2446], enabling the module to handle responses that had a `Content-Encoding` header [#1680]. Later, the project refined this process, introducing "More lenient gzip decompression" to ensure that "malformed but otherwise acceptable server responses are still properly decompressed" [#2492]. [#2492] thread by addaleax, 2016-12-21 https://github.com/request/request/pull/2492 [#2446] thread by ghost, 2016-11-02 https://github.com/request/request/issues/2446 [#1680] comment by simov, 2015-07-23 https://github.com/request/request/issues/1680#issuecomment-124089311 [#951] thread by kevinoid, 2014-07-01 https://github.com/request/request/pull/951 [b66e4cd] commit by Mikeal Rogers, 2014-07-02 https://github.com/request/request/commit/b66e4cd 39s That's a real, unedited run on my laptop. The right answer is PR #951 by kevinoid, merged in July 2014, after three earlier attempts (#303, #458, #464) were closed. The answer isn't perfect: the "x-gzip" quote comes from a later issue (#2446), not the original feature. The citation is real, so the checker can't catch that; it only guarantees that every citation points at something that exists. Code JayPokale / git-seance Ask a dead open-source project why it did what it did. GraphRAG over its history with LanceDB, lance-graph and a local Gemma; every claim cites an issue or commit. git séance 🕯️ Ask a dead open-source project why it did what it did, and get an answer that cites its own history. git séance turns a repository's issues, pull requests, comments and commits into a graph, and answers questions with a local open-weight model (Gemma 4 through Ollama) that may only speak from that history. Every claim cites an issue ([#3142]) or a commit ([a1b2c3d]), and the code removes any citation that isn't in the retrieved evidence. The first project summoned is request, the Node.js HTTP library that went into maintenance mode in 2020: 3,477 issues and pull requests, 12,441 comments and 2,290 commits. How it works Ingest (seance/ingest.py): issues, pull requests and their comments from the GitHub API, commits from a blobless clone. Build (seance/build.py): node tables Thread, Comment, Commit and relationship tables REPLIES (comment → thread)… View on GitHub How I Built It History as a graph ingest.py downloads issues, PRs and comments from the GitHub API and reads commits from a blobless clone. build.py turns them into node tables (Thread, Comment, Commit) and relationship tables, embeds every node with nomic-embed-text, and stores it all in LanceDB: relationship from → to where it comes from edges REPLIES comment → thread the comment's issue 12,441 MENTIONS commit → thread "Fixes #123" and friends in the message 806 REFERS thread → thread #123 and issue links in a thread or its comments 1,675 PART_OF commit → PR every commit of a merged PR's branch 1,002 The graph queries run with lance-graph, which executes Cypher over those Arrow tables. I've been sending fixes to lance-graph's planner, so I wanted to use it on something real: MATCH (m:Commit)-[:PART_OF]->(t:Thread) WHERE m.sha IN ['10246c8...'] RETURN t.number AS number (One honest wrinkle: lance-graph doesn't parse [:MENTIONS|PART_OF] yet, so the code runs one query per relationship type.) The first GraphRAG lost to plain search To measure anything, I wrote down 10 questions about request and checked by hand which threads answer them, then scored plain vector search against graph retrieval with the same evidence budget (16 items). My first graph version ranked whole threads and kept the top four. It did worse than plain vector search: 5/10 against 8/10. Two reasons. One-line comments like "same here" score higher than the thread that actually answers the question (0.83 against 0.71). And four threads were too few: vector search spreads its 16 items over a dozen threads. So the graph now adds to vector search instead of replacing it. Ten slots go to the nearest threads, comments and commits; six go to what the graph says matters, ranking threads by how many of their parts matched, and adding their opening post and the commit that merged them. The edge that mattered: commits belong to PRs The gzip question is the one plain search gets wrong, and it shows why the graph helps. The commit that added gzip, 10246c8 [PATCH v2] Add support for gzip content decoding, never mentions its PR. Only the merge commit does ("Merge pull request #951"). The PART_OF edge (from git rev-list merge^1..merge^2 for every merge commit) links the commit to PR #951, so the commit that vector search finds leads to the PR and its discussion. Citations are checked in code Gemma has to end every claim with a citation. A regex then checks each one against the retrieved evidence and drops any that isn't there. That check had its own bug: Gemma writes citations as [\#3142], [2780] and [#12, #34], and my first regex only accepted [#3142]. It wasn't removing anything, but it wasn't counting anything either, so the first scores said Gemma cited the right thread in 4 of 10 answers. After fixing the parser and re-checking the same saved answers: 10 questions, 16 evidence items answering thread retrieved answer cites it invented citations removed vector search only 8/10 7/10 0 graph (lance-graph) + vector 9/10 9/10 0 Gemma 4 E4B, about a minute per answer on a 6 GB GTX 1660 Ti. The caveats matter here: I chose the 10 questions and their answers myself, and I corrected one label (gzip) after seeing what retrieval found: I had first marked #303, which turned out to be a closed attempt, not the PR that landed. 10 questions is small; treat these as directional, not a benchmark. Neither mode finds the thread where Node 0.8 support was dropped (#1427). It's a three-line issue, and dozens of "doesn't work on node X" comments outscore it. "Invented citations removed: 0" means Gemma never cited something that wasn't in its evidence in these runs. It doesn't mean every sentence is right, as the demo above shows. Why Does Open Innovation Matter? The project is open, so its whole memory is open. Every issue, comment and commit of request is public. That's what makes a séance possible at all: a closed project's reasons die with its team. A local model makes re-asking free. Every number above comes from running the same questions again and again while changing the retrieval. On a paid API I'd have stopped after the first run looked fine, and I'd never have seen that my first GraphRAG was worse than plain search. Nothing to shut down. The ghost runs on Gemma, LanceDB and lance-graph on one laptop. Unlike request itself, it can't be deprecated out from under you. Where closed would win: a frontier model would write smoother answers and would probably quote the right thread more often. But the citation check matters more than fluency here: every answer can be checked against the history. Prize Categories Best Use of Gemma: Gemma 4 E4B (via Ollama) answers every question from retrieved evidence, with citations that are checked in code, and is evaluated against plain vector search on hand-checked questions. Built with heavy help from Claude Code (an AI coding agent): it wrote most of the code, chose and checked the evaluation questions, ran the evaluations, and drafted this post.

Mobile Health Clinic at Bread of Life Pantry Shoreline Media Group

Tags: #ai #python #machinelearning #datascience If you spend any time on tech Twitter or LinkedIn right now, you’ve probably seen the hype around Multi-Agent AI. Frameworks like CrewAI, AutoGen, and LangGraph promise that if you divide tasks among specialized agents: a "Planner", a "Coder", a "Reviewer", and an "Explainer" the system will magically solve complex workflows with superhuman reasoning. As a CS student, I wanted to see if the hype holds up in the real world. Does assigning distinct personas to multiple LLMs actually make data analysis more accurate than just asking a single AI? Or does it just burn API credits for fun? I built an automated data-analyst pipeline, locked it down in Docker, and tested it on 50 benchmark questions across three public datasets (tips, penguins, and taxis) against two single-agent baselines using Groq's LLaMA 3.3 70B. Here is what I built, how I set up the evaluation, and the humble reality check the data gave me. The System Architecture Before evaluating anything, I set a strict security rule for myself: The LLM should never have direct shell or code execution access. If you let an LLM generate code and run it directly on your host, you are practically asking for rm -rf / or security leaks. So I built an execution sandbox: The model never sees the entire raw CSV (which saves massive context tokens); it only receives a summary of column names, inferred data types, and the first few sample rows. I then built three configurations (arms) to test: Baseline 1 (Single Agent, 1 Attempt): A single LLM receives the schema and question, writes Python/pandas code once, and runs it in the sandbox. If it throws an error or fails, that's it. Baseline 2 (Single Agent, up to 3 Attempts): The single agent generates code. If the sandbox throws a Python traceback (e.g., KeyError or syntax error), the exact error is fed back to the same agent with: "That failed with [error]. Fix it." (Max 3 attempts). Multi-Agent Team (4 Agents): (1) Planner: Analyzes the question and outlines the pandas steps. (2) Coder: Writes the pandas script strictly adhering to the plan. ̐(3) Reviewer: Checks the code and sandbox output. If flawed, it rejects it and sends feedback back to the Coder (up to 3 iterations). (4) Explainer: Takes the verified numeric result/plot and writes a clean, plain-English summary. The 50-Question Benchmark To keep myself honest, I didn't just cherry-pick 3 prompts and call it a day. I wrote 50 ground-truth questions across four distinct categories: Calculation (27): e.g., "What is the average total_bill?", "What percentage of diners are smokers?" Lookup (11): e.g., "Which day had the highest average tip?" Charts (8): e.g., "Draw a histogram of trip distances." Unanswerable / Guardrails (4): e.g., "What is the average waiter rating?" (where the column does not exist). The AI is scored on whether it properly raises a refusal instead of hallucinating a fake number. I computed the exact ground-truth values using vanilla pandas, froze the questions file, and computed its SHA-256 fingerprint so I couldn't quietly edit questions after seeing errors. The Results: A Reality Check I ran all 50 questions across all 3 arms (150 total sandbox executions) logging latency, total tokens, USD cost, and retry counts. Here are the primary results: What happened here? Baseline 2 got a perfect 100% score. A single prompt with a dumb while-loop feeding Python error tracebacks back into the model beat the entire 4-agent collaborative team. The Multi-Agent Team cost ~3.5x more Passing context, plans, feedback, and reviews between the Planner, Coder, Reviewer, and Explainer burned 3.6x more tokens (3,543 vs. 978 mean tokens per question) and cost 3.5x as much money. The Multi-Agent Team was noticeably slower. Sequential LLM calls chained together meant average latency ballooned from ~2.4 seconds to ~4.0 seconds per query. Failure Analysis: Why Did the Team Lose? When I dug into the exact failed logs, I learned two big lessons about AI engineering: The Explainer "telephone game" (team tips-05) On question tips-05, the user asked for the average tip percentage. The Coder agent actually computed the correct pandas series in the sandbox. But when the result reached the Explainer agent, the Explainer synthesized a paragraph saying: "The row-level percentages were computed, but the average was not explicitly summarized." The multi-agent pipeline dropped the ball on synthesis even though the code ran fine. In contrast, the single agent just outputted result = df['tip'].mean() directly without losing context between agents. Why Baseline 1 failed (and why Baseline 2 fixed it) Baseline 1 failed exactly 3 questions: tips-14, tips-15, and taxis-15. All three were chart questions. The model successfully generated the matplotlib figure, but forgot to assign a return variable result = "bar_chart". Under Baseline 1, the sandbox flagged result not set, and it was scored as a failure. Under Baseline 2, the sandbox returned ValueError: The code did not set 'result', the model saw the error, added result = True on attempt 2, and passed instantly. You didn't need a Planner or Reviewer agent to fix this; you just needed execution feedback. A Fun Plot Twist: The Scorer Bug During testing, the Multi-Agent team initially scored 47/50 (94%). When I manually inspected the diff for tips-08 ("How many rows have size equal to 6?"), the expected answer was 4. Baseline output: 4 → Scorer marked PASS. Team output: "Four rows have a size of 6." → Scorer marked FAIL. My automated regex scorer was looking for digits (\b4\b), and completely overlooked written English number words. Because the multi-agent team has an "Explainer" agent that turns numbers into polite prose, the team was disproportionately penalized by the test harness! Fixing the scorer to parse number words ("four" → 4) raised the team to 48/50. Lesson: Always manually inspect your benchmark failure edge cases. Automated test harnesses can carry measurement biases. Key Takeaways for Student & Junior Devs If you are building LLM applications for hackathons, side projects, or internships: Self-Correction > Agent Teams: Before building a complex multi-agent hierarchy with LangChain or CrewAI, try a simple Generate → Execute in Sandbox → Retry on Error loop. In deterministic domains like code and SQL, runtime compiler/interpreter feedback is 10x more valuable than another LLM giving "advice". Beware the Token Tax: Every agent you add multiplies your token usage, latency, and failure points. Multi-agent coordination should only be introduced when different subtasks require genuinely different tools, isolated state, or human approval gates. Guardrails work surprisingly well: All 3 setups scored 4/4 (100%) on unanswerable questions. Prompting models with explicit instructions: "If the data cannot answer the question, raise CANNOT_ANSWER: [reason]" prevented hallucinations completely. The full project code, FastAPI server, Docker sandbox setup, and frozen benchmark results are open source on GitHub.

This Day in Country History: October 28 Backstage Country

Cipla’s US recovery, biosimilar pipeline key to future growth prospects The Economic Times

A Layton woman is accused of setting up a GoFundMe, falsely claiming she was a victim of the devastating Cottonwood Fire in Beaver County.

Nikola Jokic records 200th career triple-double in Nuggets' win over Pelicans ESPN

See where mobile home lot rent ordinances are on the ballot across Maine newscentermaine.com

Tesla Driver Accidentally Uses Emergency Drive Away [VIDEO] Not a Tesla App

Hawks’ late rally falls short against Heat despite Flemings’ career night Peachtree Hoops

Project: CyberMoranda CIDS Full Form: Cognitive Intrusion Detection System Current Stage: MVP → Evaluation & Generalization Development Environment: Android + Termux + Rust CIDS ka main idea CIDS ka basic idea simple hai: «Attacker ko ek single request se judge nahi karna, balki uske behavior aur sequence ko samajhna.» Example: Normal Request ↓ Reconnaissance ↓ Admin probing ↓ Payload testing ↓ Attack chain ↓ Risk increases ↓ Response becomes stronger Isliye CIDS ka focus sirf "bad request block karo" nahi hai. CIDS ka goal hai: Think Before You Act. Core pipeline — Complete Current architecture: HTTP Request ↓ Event Normalizer [✓] ↓ SecurityEvent [✓] ↓ Session Context [✓] ↓ Behavior Correlation [✓] ↓ Security Signals [✓] ↓ Risk Engine [✓] ↓ Risk Confidence [✓] ↓ Risk Decay [✓] ↓ Policy Engine [✓] ↓ Adaptive Enforcement [✓] ↓ Cognitive Delay / Sticky Trap [✓] ↓ Containment / Deception [✓] Ab tak kya-kya build ho chuka hai Detection & Intelligence Standardized Security Event model Event normalization Telemetry pipeline Session tracking Behavioral correlation Security signals Explainable risk verdict Risk scoring Risk confidence Temporal risk decay Response CIDS ab situation ke according response change kar sakta hai: Observe ↓ Tarpit ↓ Contain ↓ Deception Iske saath: Cognitive delay Sticky Trap Containment Deception Time-wasting Rehabilitation after risk decay bhi implemented hain. Behavioral intelligence CIDS isolated events ke bajay sequences ko analyze karta hai. Examples: robots.txt → admin Recon → Admin transition detect ho sakta hai. Multiple different JA4 fingerprints Fingerprint rotation anomaly detect ho sakti hai. Browser User-Agent + Scanner-like JA4 Possible fingerprint spoofing detect ho sakti hai. Compound behavior bhi analyze hota hai, jaise: Recon + Admin probing + Payload fuzzing Isse overall risk increase ho sakta hai. JA4 TLS fingerprinting CIDS mein JA4 fingerprint inspection ka prototype bhi add ho chuka hai. Currently CIDS: suspicious/scanner-like JA4 identify karta hai Browser UA + suspicious JA4 mismatch detect karta hai Session ke andar JA4 rotation detect karta hai Fingerprint anomalies ko behavioral signals ke saath correlate karta hai Important: JA4 ko standalone proof nahi maana gaya hai. Ye behavioral context ka ek signal hai. Deterministic Replay Framework Ek important testing system bhi ban chuka hai. Ab attack scenarios ko manually repeat karne ki zarurat nahi. Replay framework mein: predefined events timestamps expected decision expected verdict isolated sessions virtual/deterministic timing assertion checking available hain. Isse hum reproduce kar sakte hain: Same input ↓ Same timeline ↓ Same CIDS behavior Ye debugging aur regression testing ke liye important hai. FP/FN Evaluation System CIDS ke liye proper evaluation framework bhi ban gaya hai. Metrics: True Positive True Negative False Positive False Negative Precision Recall F1 False Positive Rate False Negative Rate Response Alignment Exact Decision Matching Evaluation do levels par hota hai: Step Level + Scenario Level Frozen baseline Ek baseline dataset create kiya gaya: 10 scenarios / 20 steps Isme benign, suspicious aur hostile behavior included hai. Initial baseline performance: Precision: 71.43% Recall: 71.43% F1: 71.43% FPR: 15.38% FNR: 28.57% Iteration-1 tuning ke baad: Steps: 20/20 exact TP: 7 TN: 13 FP: 0 FN: 0 Precision: 100% Recall: 100% F1: 100% FPR: 0% FNR: 0% Scenario level par bhi: 10/10 exact FP: 0 FN: 0 Lekin ek important lesson: 100% baseline score ka matlab real-world 100% accuracy nahi hai. Kyuki tuning baseline scenarios ke around hui thi. Isi wajah se holdout testing ki gayi. Holdout testing — Reality check Holdout mein 6 naye scenarios aur 16 steps the. Result: Precision: 50% Recall: 33.33% F1: 40% FPR: 7.69% FNR: 66.67% Pehle ye disappointing lag sakta hai. Lekin actually ye useful result tha. Holdout ne prove kiya ki baseline par perfect score ke bawajood model mein generalization gaps hain. Holdout se mile 3 main problems Problem 1 — Legitimate admin workflow Realistic flow: / ↓ /login ↓ /admin ↓ /admin CIDS second "/admin" ko suspicious samajh raha hai. Current reason: "is_clean_second_admin_refresh" bahut strict hai aur sirf very specific history ko clean refresh maanta hai. Problem 2 — Unknown sensitive path Holdout: /.aws/credentials CIDS ne miss kar diya. Current sensitive path detection mein limited hardcoded paths hain: /.env /.git /config /backup /wp-admin Isliye unseen sensitive paths ka detection weak hai. Problem 3 — SQL Injection variant Holdout payload: username=admin'--&password=foo Current SQLi detection mainly in patterns ko check karta hai: ' or 1=1 union Isliye ye SQLi variant miss ho gaya. Ek important problem jo actually problem nahi nikli Humein doubt tha ki fast legitimate browsing: /home /docs /pricing /contact /blog ko CIDS attack samajh sakta hai. Holdout mein ye scenario pass hua. Isliye is logic ko unnecessary modify nahi karna hai. Current Iteration-2 status Iteration 1: Baseline ↓ Tuning ↓ 100% frozen-baseline result ↓ Git commit Complete. Current Iteration 2: Holdout failures ↓ Root causes identified ↓ SQLi variant identified ← NEXT ↓ Admin workflow fix ↓ Sensitive-path generalization ↓ Regression testing ↓ Holdout retest SQLi payload already identified hai: username=admin'--&password=foo Uske liye surgical detector improvement ready hai, lekin abhi code change apply nahi kiya gaya hai. Git checkpoint Iteration-1 tuning ka commit: 6b7c29 feat(eval): add FP/FN evaluation harness, baseline corpus, and iteration-1 risk tuning Current approach: Frozen baseline ↓ Iteration-2 changes ↓ cargo test ↓ Baseline regression ↓ Holdout evaluation ↓ Review ↓ New commit Purane working baseline ko directly modify nahi karna. Current mindset CIDS ab sirf features add karne ka project nahi raha. Ab real engineering phase hai: «"Kya CIDS unseen behavior ko bhi correctly understand kar sakta hai?"» Is stage par: False Positive ko kam karna bhi important hai. Aur: False Negative ko kam karna bhi. Sirf detection badha dena enough nahi hai, kyunki overly aggressive detection legitimate users ko bhi block kar sakti hai. Goal hai: Better Detection + Lower False Positives + Correct Response Level + Explainability Abhi exactly kahan khade hain CIDS MVP [✓] Core Detection [✓] Session Intelligence [✓] Behavior Correlation [✓] Risk Engine [✓] Confidence [✓] Risk Decay [✓] Policy Engine [✓] Adaptive Enforcement [✓] Sticky Trap [✓] Deception [✓] Replay Framework [✓] JA4 Prototype [✓] Evaluation Harness [✓] Frozen Baseline [✓] Holdout Benchmark [✓] Iteration-2 Generalization 🔄 Next technical task SQLi variant detection improve karna. Uske baad: legitimate multi-step admin workflow phir: generalized sensitive-path detection. Har change ke baad: cargo test ↓ Baseline regression ↓ Holdout ↓ Only then commit One-line project status «CyberMoranda CIDS ka core MVP aur adaptive defense architecture complete hai; ab project feature-building se nikal kar evidence-based detection tuning aur generalization phase mein hai.»

Regeneron, Amgen Further Narrow Lawsuit Over Eylea Biosimilars Bloomberg Law News

A Reddit user’s firmware teardown of a bargain Android radio is a reminder of why certification and safeguards matter A Honda Civic owner recently bought a low-cost Android head unit on Amazon and decided to look at what was running under the hood. Using Claude Code, an AI coding tool, he dug into the firmware and posted his findings on Reddit. According to the post, the radio shipped with Baidu location services, a call recorder, a voice assistant that used about a third of the processor even while idle, and a script that quietly reinstalled Chinese media apps and an app store if anyone removed them. None of this was disclosed to the buyer. The story has been circulating heavily on social media and has people talking. It’s worth digging a little deeper into what it means for our industry, because this is becoming more and more apparent with this style of radio. Plenty of people ask why units from the established brands cost more. Stories like this one help answer that question. The Wild West of the Dash That’s not to say every budget unit is bad, or that none of them work. Some do. The problem is that nobody really knows. These products are cloned and sold under countless brand names, and there’s no benchmark and no independent testing behind them. A label can claim any spec at all, and the buyer would never know the difference. Too often the main goal is simply how cheaply the product can be built and shipped. Most of us know someone who bought one of these radios. It worked great for a couple of months, and then it died. What You Can’t See Can Hurt You The bigger concern is what’s happening that the owner can’t see. A head unit connected to your phone can reach your contacts, your messages, your location and the microphone in your car. When the software isn’t vetted, there’s no way to know what’s being gathered, where it’s going or how it might be used. The Civic owner only found out because he went looking. Why Certification Costs Money There’s a lot of frustration in the industry about the certification hoops the major brands have to jump through, along with the cost those hoops add. Before we pile on, it’s worth stepping back. Certification, compliance testing and software safeguards exist for a reason. They’re part of why a name brand unit costs more, and they’re part of what the customer is paying for: a product that does what it says, keeps its hands off their data, and has a company standing behind it. The Takeaway for Retailers This is a conversation worth having at the counter. When a customer asks why a unit costs more than the one they saw online, the answer isn’t only sound quality or features. It’s knowing exactly what’s going into their car and what isn’t. Maybe there’s a reason it’s so cheap. Credit to The Drive for breaking this story. The post Cheap Head Units and the Hidden Cost of “Uncertified” appeared first on ceoutlook.com.

A new Sandoz analysis reveals the next wave of affordable medicines is coming; will Canada be ready? Tolerance.ca

Valve says a "significant number of people" are tackling Steam's AI slop problem, though it refuses to gatekeep which tools developers can use. Read more...