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AI 资讯

I Made a Battery Admit It Was Only 73% Healthy — On-Device, End to End

Voltage lies. Put a battery under load and its terminal voltage sags. Let it rest and the voltage springs back. A naive fuel gauge watching only voltage will happily tell you a worn-out cell is "fine" right up until it falls off a cliff. The number you actually care about — is this battery still good, or is it time to replace it? — isn't in the instantaneous voltage at all. It's in the capacity : how much charge the cell can still deliver between full and empty. That quantity fades as a cell ages. Tracking it is called State of Health (SoH) , and it's the difference between "the device says 80%" and "the device has 80% of the runtime it had when it was new." I wanted my open-source battery SDK ( ibattery-sdk , Apache-2.0) to learn SoH on the device itself — no cloud model, no floating-point, on MCUs with kilobytes of RAM. This post is the story of getting that working end to end: from a coulomb integral in firmware to a faded value showing up live on a Grafana dashboard. The idea: learn capacity from one full→empty trip You don't need a PhD-grade model to estimate usable capacity. You need two anchors and an ammeter. Full anchor — when the cell is at its full-voltage plateau, declare "this is full" and set the coulomb counter to the rated capacity. Discharge — integrate current over time (coulomb counting). Every milliamp-hour that leaves the cell ticks the counter down. Empty anchor — when the cell hits its empty-voltage threshold, look at how much charge actually flowed. A healthy cell delivers close to its rated capacity before going empty. An aged cell hits empty early — it simply has less to give. From the charge measured between those two anchors, you get the cell's real usable capacity, and SoH = measured / rated . The SDK runs it through an integer EMA (so one noisy excursion doesn't whip the estimate around) and a plausibility guard (reject anything outside 30–120% of rated — that's almost certainly a glitch, not a real measurement). The whole thing is inte

2026-06-05 原文 →
AI 资讯

Smart Lighting Protocol Showdown: Zigbee vs Matter vs BLE Mesh (2026)

Smart Lighting Protocol Showdown: Zigbee vs Matter vs BLE Mesh (2026) After deploying thousands of Zigbee smart lights through our manufacturing line at nexLAMP, and watching countless customers struggle with protocol selection, I decided to write this practical comparison. The Real Problem "My smart lights keep disconnecting! I think I chose the wrong protocol..." This is the #1 complaint I see on Reddit, Xiaohongshu, and Zhihu. The fix isn't a better router — it's choosing the right protocol from day one. Protocol Deep Dive Zigbee — The Workhorse Frequency : 2.4 GHz (separate from WiFi) Topology : Star + Mesh hybrid Max devices : 200+ per coordinator Latency : 50-200ms Cost/unit : ~$3.5-5.0 (Tuya Zigbee drivers) Why it wins for lighting: Each node is a repeater → self-healing mesh Ultra-low power → years on coin cell for sensors Mature ecosystem → Tuya, Hue, Aqara, Xiaomi all ship Zigbee The catch: You need a Zigbee gateway (~$15-20). This is the only upfront cost. BLE Mesh — The Budget Option Frequency : 2.4 GHz (shared with WiFi/BLE) Topology : Managed flood mesh Max devices : ~50 (practical limit ~30) Latency : 100-500ms (increases with node count) Cost/unit : ~$2.0-3.5 The flooding problem: Every command is broadcast to every node. With N nodes, you get O(N²) message propagation. Past 30 devices, you'll notice visible lag. Good for: Small apartments (≤ 6 lights), budget projects. Matter — The Future Transport : Thread (preferred) or WiFi Topology : Thread mesh (similar to Zigbee) Max devices : 250+ (theoretical) Latency : 30-150ms (Thread), variable (WiFi) Cost/unit : ~$7.0-11.0 (currently higher) Matter's promise is genuine cross-platform control. But in 2026: Pros: Native HomeKit, Alexa, Google Home support Thread mesh is excellent (when it works) IP-based → easier cloud integration Cons: Thread Border Routers aren't ubiquitous yet Advanced lighting features still evolving Premium pricing for early adoption Cost Analysis (20-Fixture Deployment) Protocol Driv

2026-06-03 原文 →
开发者

Java on Raspberry Pi: Rediscovering Java Beyond the Enterprise

When most people think about Java, they immediately picture enterprise applications, banking systems, massive backend services, or decades-old corporate software. While Java has earned its reputation in the enterprise world, that is only part of the story. Today, Java can run on devices as small as a Raspberry Pi, opening the door to hardware projects, edge computing, home automation, education, and hands-on learning experiences. Combining Java with Raspberry Pi creates a powerful platform for experimentation, learning, and building real-world solutions that go far beyond traditional enterprise development. Raspberry Pi teaches us about hardware. Java allows us to apply professional software engineering practices to that hardware. Together, they create a powerful platform for learning, prototyping, and building real-world IoT and edge computing solutions. Java Is More Than Enterprise Software Java's enterprise success has sometimes created the misconception that it only belongs in large organizations. In reality, modern Java offers: Excellent support for Linux and ARM architectures. High performance and low resource consumption. Modern frameworks such as Spring Boot, Quarkus, and Micronaut. Strong support for IoT and edge computing. Access to hardware through mature libraries. One of the largest developer ecosystems in the world. The Raspberry Pi highlights a different side of Java—one focused on creativity, experimentation, and direct interaction with the physical world. Instead of building another web application, you can build systems that sense, react, and interact with their environment. Java at the Edge One of the most exciting technology trends today is Edge Computing. Traditionally, devices send data to cloud services where processing and decision-making occur. Edge computing shifts part of that processing closer to where the data is generated. A Raspberry Pi running Java can: Process sensor data locally. Apply business rules before sending information to th

2026-06-02 原文 →
AI 资讯

The deadly Ebola outbreak is proving difficult to control

The alert was raised on May 5. Four health-care workers in the Ituri Province of the Democratic Republic of the Congo had died from an unknown illness within four days. Rapid response teams were sent to investigate, and tests at a research center in Kinshasa revealed the culprit: the Bundibugyo virus, one of the viruses…

2026-05-29 原文 →
AI 资讯

Sniffing Modbus Traffic with 5 Lines of Python (And Why It Should Scare Your OT Team)

⚠️ For defensive/educational purposes only. Sniff only networks you own or are explicitly authorized to test. Unauthorized network monitoring is illegal in most jurisdictions. The uncomfortable truth about your factory floor If your plant uses Modbus TCP — and statistically, it probably does — every register read, every coil write, every sensor value is flying across your network in plaintext . No encryption. No authentication. No signature. Nothing. Modbus was designed in 1979 by Modicon for serial communication between a PLC and a few field devices on a dedicated cable. The threat model was "someone might physically tap the wire." The solution was "don't let strangers into the control room." Forty-five years later, that same protocol is running over your corporate VLAN, talking to cloud historians, and occasionally — if your IT/OT segmentation has gaps — reachable from the internet. Let me show you what that looks like from the wire. The 5-line sniffer This is a defensive monitoring tool. Same code your blue team would use to baseline normal traffic and detect anomalies. Requires scapy : pip install scapy from scapy.all import sniff , TCP , Raw def show_modbus ( pkt ): if TCP in pkt and pkt [ TCP ]. dport == 502 and Raw in pkt : payload = pkt [ Raw ]. load print ( f " { pkt [ ' IP ' ]. src } → { pkt [ ' IP ' ]. dst } : { payload . hex () } " ) sniff ( filter = " tcp port 502 " , prn = show_modbus , store = False ) Run it on a span port, a TAP, or a mirror VLAN, and within seconds you'll see something like this: 192.168.1.50 → 192.168.1.10: 0001000000060103006400 02 192.168.1.10 → 192.168.1.50: 00010000000701030441f00000 192.168.1.50 → 192.168.1.10: 00020000000601100065000102 Every byte tells a story. Let's decode the first packet. Decoding what you just captured The Modbus TCP frame format is documented in the spec (it's public — that's part of the problem): Bytes 0-1: Transaction ID Bytes 2-3: Protocol ID (always 0x0000 for Modbus) Bytes 4-5: Length Byte 6: Unit

2026-05-28 原文 →
AI 资讯

TechCrunch Disrupt 2026 Early Bird ticket savings end in 3 days

There are only 3 days left to save up to $410 on your ticket to TechCrunch Disrupt 2026. Early Bird pricing ends May 29 at 11:59 p.m. PT, and once the deadline passes, ticket prices increase. If you plan to attend one of the most influential gatherings in tech this year, now is the time to lock in your pass before rates go up again.

2026-05-27 原文 →
AI 资讯

The Enhanced Games fit right in with the rest of 2026’s longevity vibes

This Sunday, a group of 42 athletes will gather in Las Vegas to compete in a somewhat unusual sporting competition. Participants in the inaugural Enhanced Games are being encouraged to take performance-enhancing drugs. The goal is to “push the boundaries of human performance.” The games’ organizers have said that competitors will only be taking substances that…

2026-05-22 原文 →
AI 资讯

Alleged Kimwolf Botmaster ‘Dort’ Arrested, Charged in U.S. and Canada

Canadian authorities on Wednesday arrested a 23-year-old Ottawa man on suspicion of building and operating Kimwolf, a fast spreading Internet-of-Things botnet that enslaved millions of devices for use in a series of massive distributed denial-of-service (DDoS) attacks over the past six months. KrebsOnSecurity publicly named the suspect in February 2026 after the accused launched a volley of DDoS, doxing and swatting campaigns against this author and a security researcher. He now faces criminal hacking charges in both Canada and the United States.

2026-05-22 原文 →
AI 资讯

Anti-DDoS Firm Heaped Attacks on Brazilian ISPs

A Brazilian tech firm that specializes in protecting networks from distributed denial-of-service (DDoS) attacks has been enabling a botnet responsible for an extended campaign of massive DDoS attacks against other network operators in Brazil, KrebsOnSecurity has learned. The firm's chief executive says the malicious activity resulted from a security breach and was likely the work of a competitor trying to tarnish his company's public image.

2026-04-30 原文 →
AI 资讯

Russia Hacked Routers to Steal Microsoft Office Tokens

Hackers linked to Russia's military intelligence units are using known flaws in older Internet routers to mass harvest authentication tokens from Microsoft Office users, security experts warned today. The spying campaign allowed state-backed Russian hackers to quietly siphon authentication tokens from users on more than 18,000 networks without deploying any malicious software or code.

2026-04-08 原文 →