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A lesson in overcorrection, from an AI-assisted builder who is scared stiff of shipping spaghetti and...

I wouldn’t call it cheap, but this monitor from Asus brings proper HDR-capable video editing to creators who couldn’t previously afford an OLED display.

Apply before September 4 to be a part of the TechCrunch Disrupt community by hosting your own Side Event.

I have submitted a paper to acml 2026 journal track, the official date of release of review is 27 August, but I have not heard anything from them, if anyone received the review then let me know I will write to program chairs. Thanks submitted by /u/Jealous_Key_4030 [link] [留言]

3D Object Detection for Physical AI Applications A robot needs more than image classification. It needs to know: What object is present? Where is it? How large is it? How is it oriented? 3D object detection answers these questions in physical space. 3D Detection Pipeline Camera / LiDAR | v Preprocessing | v Feature Extraction | v 3D Detection Model | v 3D Bounding Boxes | v Tracking / Planning A 3D bounding box can contain: (x, y, z) (width, height, depth) (rotation) (class) (confidence) LiDAR-Based Detection LiDAR naturally provides 3D geometry. A typical pipeline is: Point Cloud | v Filtering | v Voxelization / Features | v Neural Network | v 3D Boxes Challenges include sparse points and computational cost. Camera-Based Detection A camera provides dense visual information. Monocular 3D detection tries to infer depth from a single image, while stereo systems can estimate depth geometrically. Multi-Modal Detection Combining cameras and LiDAR can provide both semantics and geometry: Camera ---> Visual Features --+ | LiDAR ----> 3D Features ------+--> Fusion --> 3D Detection This is useful for autonomous robots operating around people, vehicles, and objects. Post-Processing Raw detections are often filtered using: Confidence thresholds Non-maximum suppression Geometric constraints Temporal tracking Tracking can stabilize detections across frames. ROS 2 Integration A practical architecture: /sensors/camera /sensors/lidar | v /3d_detector | v /objects_3d | +--> /tracker | +--> /planner Use standardized message structures where practical so perception remains decoupled from planning. Measuring Performance Evaluate: Precision Recall 3D IoU Position error Orientation error Inference latency FPS For physical AI, latency matters almost as much as accuracy. A detector that is accurate but too slow can still be unsuitable for a moving robot. Production Considerations Test across: Day/night conditions Different sensor placements Partial occlusion Different object sizes Dynamic

Building a Real-Time SLAM System for Mobile Robots SLAM means Simultaneous Localization and Mapping . A mobile robot must answer two questions: Where am I? What does the environment look like? The challenge is that the robot needs the map to localize while also needing localization to build the map. SLAM Architecture Sensors | +--> Frontend | | | +--> Odometry | +------------------+ v State Estimator | v Map Builder | v Map Sensor Options Typical systems use: 2D LiDAR 3D LiDAR Cameras IMUs Wheel encoders The right sensor combination depends on the environment. SLAM Frontend The frontend extracts motion constraints. For LiDAR: Scan | v Feature / Point Processing | v Scan Matching | v Relative Motion For visual SLAM: Image | v Feature Extraction | v Feature Matching | v Relative Pose Backend Optimization The backend can represent the robot trajectory as a graph: Pose 1 ---- Pose 2 ---- Pose 3 ---- Pose 4 \ / +------ Loop Closure ---+ Loop closure recognizes that the robot has returned to a previously observed location. This can significantly reduce accumulated drift. Real-Time Constraints SLAM is not useful if it produces excellent maps several seconds too late. Monitor: Sensor processing latency Pose estimation latency Map update time CPU/GPU utilization Queue sizes Frame/scan drops Map Resolution Higher resolution gives more detail but costs more memory and computation. Choose resolution based on: Robot size Environment Navigation requirements Available compute Failure Modes SLAM can struggle with: Repetitive environments Dynamic objects Feature-poor walls Rapid motion Poor sensor calibration Incorrect timestamps A robust system should monitor confidence and detect tracking failures. Production Pipeline Camera / LiDAR / IMU | v Sensor Calibration | v Odometry Frontend | v Pose Estimation | v Loop Detection | v Graph Optimization | v Map Server | v Navigation The goal of production SLAM is not just map quality. It is stable localization, predictable latency, and grac

The seed round for the app that provides real-time legal and policy guidance to officers was led by SignalFire and Las Olas VC.

It sure seems like it. The stores confirmed to be affected include Fort Irwin , Calif.; F.E. Warren Air Force Base , Wyo.; Fort Huachuca , Ariz.; Naval Station Newport , R.I.; Columbus Air Force Base , Miss.; and Travis Air Force Base , Calif., according to announcements made online by each installation. Naval Air Station Lemoore, Calif., also experienced an outage, according to M. Elizabeth, writer of the Substack newsletter Signal and Silence . Each service declined to answer questions about how many bases are affected by the outages, referring all questions to the Defense Department. Pentagon officials did not respond to questions...

The company, which distributes medicines and medical devices to hospitals and healthcare practices across the U.S., said it was hacked and expects intermittent service degradation.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. By now you’ve probably heard about last month’s major AI security incident, in which OpenAI agents escaped their sandbox and hacked into the AI platform Hugging Face while trying to cheat on…

The controversial founder of the checkout startup once valued at $11 billion is putting in $5 million of his own money.

Automakers keep shoving more tech into their cars, despite evidence that consumers are increasingly fed up with huge screens and glitchy software. In fact, the features that car owners appreciate the most happen to be the ones they barely notice, according to the latest tech survey from JD Power. The consumer research firm surveyed 68,084 […]

Tim Cook’s farewell memo to Apple employees offers a glimpse at how he wants John Ternus to be seen: as a product builder with deep experience across the iPhone, Mac, AirPods, and other major hardware lines.

Sliding Window Attention with sinks, one of the simplest existing fixes for the quadratic-cost problem in LLMs, holds up as well or better than the linear-attention variants labs have been spending post-training compute to produce. That is the claim of a [new arXiv preprint]( https://arxiv.org/abs/2608.28444 ) by Alexia Jolicoeur-Martineau, Rhea Sanjay Sukthanker, Pashmina Cameron and Emy Gervais. On the long-context reasoning benchmarks the paper singles out, the gap is not close. "SWA achieves massively higher performance (2 to 10 times higher than linear attention)," the abstract reports, naming Needle-in-a-Haystack and BABILong as the two tasks. The pitch is that the whole post-training-to-linear pipeline has been benchmarked against the wrong thing. "This line of research has not been properly compared to simpler baselines," the authors write. Their alternative needs no post-training, runs fast, and holds memory low. The recommendation is blunt: "we strongly recommend switching to SWA instead of post-training linear models." Linear attention, the abstract concedes, "may have shown some promise, but they likely require to be trained from scratch or extensive post-training in order to even match SWA." --- submitted by /u/Justgototheeffinmoon [link] [留言]

Several Garmin watches offer a built-in LED flashlight, but even if you have another brand, you likely have a decent light on your wrist.

We noticed our anti-money laundering models were performing suspiciously well. After digging into standard baselines on dynamic graphs, we found widespread temporal leakage in message-passing. If you train a GNN on a static snapshot of a dynamic graph, your model is likely cheating by seeing future edges during training. We got sick of reviewing papers with broken evals, so we released SynthFin-AML v10.0 (100k nodes, 1.2M edges) to force strict causal boundaries. The Temporal Leakage Trap Standard transductive random splits fundamentally fail on financial transaction networks because they violate the arrow of time. If Node A sends funds to B on Day 2, and B to C on Day 10, a standard 2-hop GNN will pull the Day 10 edge into the loss calculation for Day 2. The model literally looks into the future to compute embeddings. The Fix: 3-Snapshot Architecture To stop the model from cheating by looking ahead at the transaction graph, we enforced a strict 3-snapshot point-in-time split: Train Graph (Edges ≤≤ Day 7) Val Graph (Edges ≤≤ Day 8) Test Graph (Edges ≤≤ Day 10) By physically disjointing the temporal windows, we bound the receptive field of the GNN to the true causal horizon. Graph vs Tabular Reality Check Most synthetic datasets suffer from distribution leakage, where fraud transaction amounts are statistically separable from normal retail traffic. We killed the "amount split cheat" by ensuring fraud and retail transaction amounts share the exact same lognormal distribution (μ=8.517,σ=0.8 μ =8.517, σ =0.8). With tabular leakage fixed, we benchmarked a tuned LightGBM against GraphSAGE to see if the GNN overhead actually pays off for AML. We engineered 11 point-in-time graph features (Weighted PageRank, neighbor volume aggregates) for the tree model. Results (PR-AUC on strict temporal split): LightGBM (11 features): 0.848 GraphSAGE (Inductive): 0.881 Spoiler: GraphSAGE barely beats trees here unless your edge features are incredibly dense. The gap isn't astronomical, b

The three-year-old startup says it reached $15 million in ARR and profitability before raising its latest $15 million round.

For realtime release compatibility in a delivery tracking map, scale event delivery with a durable, ordered log per delivery and treat every browser connection as a disposable projection of that log. Presence can guide fan-out and capacity planning, but it must never decide whether a location update exists. Short answer: release compatibility comes from versioned envelopes, resume cursors, and an explicit resync path; scaling comes from partitioning by delivery ID and coalescing map updates at the edge, not from trusting a long-lived connection to carry every event exactly once. This decision targets an e-commerce tracking experience in which a shopper may open a map, lose connectivity in a tunnel, return on another network, and also join a delivery-specific support chat room. The deciding constraint is presence accuracy: an online indicator is useful only when its expiry rules are understood, while the delivery state must remain correct even when that indicator is late. A green dot isn't a commit log. How should realtime release compatibility scale event delivery in a delivery tracking map? Separate the system into three contracts: durable delivery state, transient room presence, and the connection used to move updates. The first contract owns truth. The second answers a narrower question: which sessions have renewed a lease recently enough to be considered reachable? The third may disappear at any point and should be replaceable without changing either of the other two. For each delivery, append an event with a monotonically increasing sequence within that delivery's partition. The client persists the last applied sequence and includes it when reconnecting. If retained events cover the gap, the server replays them in order; if they don't, the server returns a fresh snapshot plus its sequence. This is at-least-once delivery with idempotent application, which means duplicates are ordinary and gaps are detectable. It does not promise global order across unrelated del

Have you ever wondered why we keep learning advanced things that probably might not be applied properly where we came from? As someone who came from a developing country where resources are not being served on a gold plate. In fact, even if you have all the necessary knowledge to make a change but one thing comes up with no answer, how can we implement our knowledge gained abroad with no funding and no equipment to help us contribute to the blooming of our beloved country? I guess our parents worked hard to actually send us abroad, not to return to our country but instead to find a way to make a living where God destined us to go. This is not because they hate our homeland, but they feel there is no way things can change where corruption and unemployment reign to a high degree. Therefore, this is the time where advanced technologies must not be seen as burdens in a developing country. Share answers on this particular spec. Thanks!