You can view the video here.

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For availability and a recommendation for an excellent tutorial, check out this video: https://youtu.be/qxz3dbpywNM

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If you work with the Arduino Giga platform, then you know how difficult it can be to work with the Giga Display Shield attached to the Arduino. Adding DuPont connectors to the Arduino with the display connected is, well… challenging, to say the least. Well now, you don’t have to.

The GigaLab

GigaLab is a 3D printed device which allows you to use your Arduino and Display Shield, side-by-side. And, best of all, it is free. It can be downloaded from Maker World using this link.

Just think, no obscured connections, fiddly wiring or scratched display screen. The GigaLab also offers a useful cable storage solution and a place for tools and components.

In case you are wondering how best to wire the Arduino to the Display Shield, you are not alone. I have a video coming out withing the next week or two, demonstrating the technique. The video will also introduce a new plug design called the Pog Plug. The Pog Plug was inspired by the GigaLab project, so it all fits together nicely.

If you subscribe to my YouTube channel, you will get notified as soon as my video is released.

For more details on the GigaLab project, click here.

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Browsers differ in how they protect users from trackers malware fingerprinting and data collection. My top picks balance built in protections update cadence and transparency about data practices to give users real world security without sacrificing usability.

Why browser security matters

Browsers are the gateway to the web, and they handle passwords payment details and personal identifiers. A secure browser reduces exposure to phishing drive by downloads malvertising and stealthy cross site tracking while also limiting the data available for fingerprinting and third-party harvesting.

Top choices

Brave

Brave blocks ads trackers and many fingerprinting vectors by default and offers private tabs that can route through the Tor network for stronger anonymity. Its active stance on removing Google integrations in its Chromium base makes it a strong practical choice for privacy conscious users.

Firefox

Firefox combines a long history of privacy focused features with frequent security updates and a clear open source development model. It is particularly flexible because users can harden privacy settings and add audited extensions without relying on a corporate data collection model.

Tor Browser

For threat models that require strong anonymity Tor Browser remains the gold standard because it isolates traffic through the Tor network and resists fingerprinting and many deanonymization techniques. It sacrifices some convenience, but it is unmatched for hiding traffic sources.

Chromium based mainstream browsers

Chrome and Chromium descendants provide strong sandboxing and rapid patch cycles which protect against many exploit classes. However mainstream versions often include tighter integration with advertising and telemetry ecosystems which can weaken privacy by default.

Comparison chart

Conclusion

If you want practical strong protection with minimal setup, choose Brave. If you want a balance of privacy customization and extension support, choose Firefox. If true anonymity is required use Tor Browser. If you prioritize compatibility and fast fixes use Chrome while applying privacy hardening tweaks. For most users a two-browser approach works well one for everyday convenience one for sensitive tasks.

Sources: NordVPN. ZDNet. SafetyDetectives.

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The new Copilot 2025 Fall Update features also up the ante in terms of capabilities and the accessibility of generative AI assistance from Microsoft to users, so businesses relying on Microsoft products, and those who seek to offer complimentary or competing products, would do well to review them.

Read the full article here.

Source: VentureBeat.com

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Every boom likes to pretend it is different. The AI surge insists it is inevitable progress wrapped in exponential curves, not a bubble. Yet the jargon, the easy capital, and the copycat product roadmaps look very familiar. When this bubble bursts, the fallout will not be a single pop. It will be a long hiss. Here is what that will look like, why some parts of the ecosystem will be just fine, and where real value will quietly consolidate.

Funding will snap from story to substance

Capital always chases narrative until it chases revenue. We will see a sharp repricing of private companies that raised at peak multiples on the strength of slideware benchmarks. Bridge rounds will come with tougher terms and liquidation preferences that sting. Survival will depend on proof of durable unit economics, repeat customers, low churn, and gross margins that do not collapse under GPU bills. Startups that rely on perpetual subsidy to keep usage afloat will not make it. Those with minimal customer issues and disciplined cost control will.

The compute bill will come due

Cloud credits are not a business model. As subsidies fade, many teams will learn that their cost of goods sold is effectively the price of someone else’s datacenter. That pushes companies to either raise prices or invent cheaper ways to deliver similar outcomes. Expect a scramble toward smaller, task specific models, on device inference where latency and privacy matter, and ruthless pruning of nonessential features. The winners will treat compute as a precious resource, not a blank canvas.

Horizontal tools will give way to vertical stacks

Generic chatbots and “AI for everything” will feel like the social media dashboards of a past era. Useful at first, then undifferentiated. Value will migrate into vertical stacks that own workflow, data, and integration; radiology reporting that fits the hospital information system, claims processing that speaks the insurer’s rules, developer tools that plug into build pipelines and ticketing. These stacks will look boring. They will also capture the profit that general chat apps cannot defend.

Data moats will trump model bloat

Bigger models get headlines. Better data gets contracts. When budgets tighten, enterprises will ask how models were trained, how outputs can be traced, and how liability is handled. Vendors who can secure, clean, and govern domain specific data will beat vendors who only promise larger checkpoints. Expect more partnerships where customers keep their data inside their own cloud boundary, with models that visit the data rather than the reverse. Trust will be as important as accuracy.

Compliance will graduate from cost center to moat

Regulation always arrives late, then all at once. Once it does, compliance maturity becomes a competitive edge. Auditable pipelines, red teaming practices, provenance of training data, and incident response will matter in sales cycles. Vendors who baked in governance will pass due diligence without drama. Others will bolt on policies and bleed time. The bubble’s end will reward the dull habits of good engineering.

The talent market will rebalance

Right now, salaries reflect scarcity and hype. Some researchers will move from frontier work to applied roles. Many product teams will shrink. On the flip side, managers will hire for outcomes rather than buzzword familiarity. Engineers who can ship reliable systems and measure value will have the advantage. So will technical writers, designers, and support teams who can make complex capabilities usable and safe.

Consumers will keep what feels magical and drop the rest

A correction does not erase demand. It clarifies it. People will pay for tools that save time in ways they can feel; better search inside their own files, assistants that actually do the multi step tasks they promise, creative tools that produce results rather than add novelty for its own sake. Subscriptions for “AI sprinkled here” will disappear. Subscriptions for “this turns three hours into thirty minutes” will stay.

Open source will harden

The open ecosystem will look messy during the bust. Projects will be abandoned. Repos will go quiet. Yet the remaining set will mature and stabilize around pragmatic goals; performance on commodity hardware, privacy by default, and simple deployment. Community efforts that provide clear licensing, reproducible builds, and strong documentation will thrive.

The big platforms will consolidate quietly

Expect more acquisitions, more bundling of model credits into existing enterprise contracts, and more integrated stacks that make it easier to choose the house solution. For many buyers the safest choice in a downturn is the vendor they already pay. That is not always the most innovative option, but it is the one that clears procurement.

What to build in the chill that follows

1. Pick pain, not puzzles. Start with a costly bottleneck and design backward from a measurable win. Benchmarks are not the product. Throughput, error rate, and cycle time are.
2. Design for cost gravity. Assume compute will stay expensive relative to price sensitivity. Cache, distill, prune, and run smaller where possible.
3. Own the last mile. The moat is often workflow integration, change management, and trust. Get excellent at onboarding, metrics, and support.
4. Invest in data health. Provenance, labeling quality, deduplication, and feedback loops pay compounding dividends. Treat them as product features.
5. Practice resilience. Feature flags for rapid rollback, and clear incident playbooks will save you should things go awry.

Conclusion

Every technology wave overshoots. Then it right-sizes and becomes infrastructure. The internet did. Mobile did. Cloud did. AI will too. The end of the bubble will not be the end of value. It will be the end of delusion. What remains will be tools that make people better at their work, services that reduce complexity, and systems that act with reliability and care. That is not as loud as the boom phase. It is, however, more durable.

The best time to build sober companies is after the party. The music fades, the lights come on, and you can finally see who is still standing. Build for that room.

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Content ID: Progress, Power, and the Price of Automation

YouTube’s Content ID is, in spirit, a fairness machine: fingerprint the world’s audio-visual works, scan uploads in milliseconds, and route money or control back to the rightsholder. In practice, it’s also a power machine, one that’s undeniably reduced rampant infringement at internet scale, yet still concentrates leverage in the hands of large catalog owners while creators shoulder the false positives and friction that come with industrial-grade automation.

The good (and it’s genuinely good)

First, credit where it’s due. Content ID solved a problem that DMCA takedowns never could: speed and scale. Instead of whack-a-mole notices, rights holders can automatically block, track, or monetize matches, even across millions of uploads. That shift is why you can find so many legitimate remixes, commentary videos, and fan edits that are monetized rather than nuked. It’s a pragmatic middle ground: the music label gets a slice, the uploader keeps the video online, and viewers get culture in motion rather than culture behind walls.

It also quietly aligns incentives. When a fan uses a song in a travel vlog, the label earns long-tail revenue and free discovery. When a gaming channel includes a snippet of a trailer, the studio can choose to monetize rather than censor. As a result, the platform’s commons, the shared space where memes, criticism, education, and entertainment cross-pollinate, stays richer than it would under pure takedown regimes.

The bad (and it’s stubborn)

But Content ID is not neutral. Access to the system isn’t universal; it’s skewed toward large catalogs and trusted partners, which means the ability to automate control, and to monetize other people’s uploads, sits with incumbents. Small creators and independent musicians often live on the other side of that gate, where the best you can do is dispute after the fact. When a claim is wrong (and wrong claims are not rare), time is the tax: demonetization during the most lucrative early views, stress, and a dispute process that can feel like you’re appealing to the same party that just flagged you.

There’s also the “snippet paradox.” Fair use is contextual, a 10-second clip in a harsh critique is fundamentally different from the same clip in a reupload, but algorithms don’t read context, they read fingerprints. Content ID can detect matching pixels and waveforms with uncanny precision, yet it can’t weigh purpose, transformation, or commentary. That leaves creators in a gray zone where being right on principle doesn’t prevent you from losing revenue in practice.

There’s also weaponization. Bad actors sometimes upload others’ work first to “own” the fingerprint, or deploy claims to harass critics. These are edge cases relative to total volume, but for individual channels they’re existential.

The messy middle (and why it’s worth fixing)

Despite those flaws, the alternative isn’t appealing. Without automation, enforcement collapses into slow legalism, and platforms resort to blunt takedowns to avoid liability. Content ID is the best iteration we’ve seen of a difficult bargain: keep the ecosystem open whilst ensuring creators and rights holders get paid. The job now is to rebalance who benefits and who bears the costs.

What would better look like?

1. A true appeals firewall. Disputes shouldn’t feel like emailing the sheriff who wrote the ticket. Independent, time-bounded review, with penalties for repeat false claims, would raise trust on both sides.
2. Creator-side fingerprinting at smaller scales. Indie artists and mid-size channels should be able to protect their catalogs without needing label-level access. If YouTube can verify identity for monetization, it can verify ownership for fingerprinting.
3. Context-aware tooling. Even basic signals (voiceover dominant, frame-within-frame, rapid cuts, commentary language) could trigger a “likely fair-use” review path or temporary revenue escrow rather than a default claim.
4. Transparent revenue escrow during disputes. Lock earnings during the review window and release them to the final winner, with interest. That removes the incentive to file shaky claims just to capture early CPMs.
5. Audit trails and reputation scores. Make claimant accuracy visible. Frequent over-claimers should lose privileges; consistently accurate claimants could gain streamlined workflows.
6. Education and presets for creators. Built-in guidance (“this use is high-risk,” “try shorter duration,” “transformative commentary tips”) would reduce accidental flags and improve media literacy.

Conclusion

Content ID is both a triumph of engineering and a reminder that perfect detection doesn’t equal perfect justice. It’s kept the world’s biggest video commons viable while returning meaningful money to rights holders. But until access is more equitable, disputes more independent, and context better respected, creators will continue to see it as a powerful system that sometimes treats them as collateral damage. The next iteration shouldn’t be about catching more matches; it should be about catching more fairness.

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On October 7, 2025, Qualcomm announced its acquisition of Arduino, the open-source microcontroller pioneer with a global community of 30-plus million users. Arduino says the brand and open ethos will remain, and the deal still requires regulatory approval. Alongside the news, the companies unveiled the Arduino UNO Q, a dual-brain board that pairs a Qualcomm application processor with a low-power STM32 microcontroller, plus a new App Lab IDE aimed at Linux, RTOS, and Python workflows.

The big picture

From hobby table to factory floor. Qualcomm’s play is to turn Arduino, already the de facto on-ramp to embedded systems, into a broader “edge AI” funnel that stretches from weekend projects to commercial products and industrial IoT. Expect tighter links between Arduino hardware/software and Qualcomm’s AI toolchains, vision/audio accelerators, connectivity stacks, and cloud partners.

Arduino stays Arduino. Public statements emphasize continuity: open-source hardware/software, cross-vendor support (not just Qualcomm chips), and community stewardship. Branding remains independent. That reassurance matters for classrooms, labs, and makerspaces that rely on Arduino’s approachable ecosystem.

What changes for your bench

1) New performance tier, without abandoning “blink”

The UNO Q brings Linux, GPU-class graphics, and on-board AI inference to a form factor the community knows, yet it still includes a microcontroller for deterministic I/O. In practice, this means running camera pipelines, lightweight LLMs, or audio DSP on the app processor while the MCU handles precise timing for sensors and motors. The prices are $44 for the 2 GB/16 GB model and $59 for the 4 GB/32 GB model.

2) A smoother path from prototype to product

Qualcomm’s silicon catalog (wireless, vision, power) plus acquisitions like Foundries.io and Edge Impulse give Arduino projects a clearer upgrade path: start with UNO Q + App Lab, then transition to module-based designs or SOMs with long-term support. For small teams, that could cut months from proof-of-concept to pilot.

3) Tooling that speaks both “Arduino” and “Linux”

App Lab aims to bridge Arduino-style sketches with Linux apps, Zephyr/RTOS, and Python, which is useful if you’ve outgrown single-core MCUs but don’t want the friction of switching ecosystems. Expect better debugging, package management, and model deployment workflows out of the box.

What probably won’t change (and why that matters)

• Community libraries and shields should remain compatible; Arduino explicitly says its platform will continue to support multiple chip vendors. That helps protect your existing code and course material.
• Open designs and permissive licensing are core to Arduino’s identity and are being reiterated post-announcement. Keep an eye on contributor policies, but wholesale licensing shifts look unlikely in the near term.

Sensible cautions for makers

• Availability vs. hype. Early boards can sell-out and software can lag, so plan buffer time before committing to demos or curricula. The companies say pre-orders are open now, with first shipments in October/November and more by year-end. Verify dates for your region.
• Watch licensing fine print. If you release commercial add-ons or redistribute firmware blobs, double-check new SDK terms, especially around AI accelerators and wireless stacks where proprietary bits are common. (This is a general best practice; no adverse changes have been announced.)
• Vendor lock-in concerns. Arduino and Qualcomm say cross-vendor support continues, but keep your designs portable: abstract hardware, avoid one-off proprietary APIs unless you need them, and maintain MCU-first fallbacks.

Who benefits most, right now?

• Educators & beginners: Same approachable entry point, plus a migration path to AI/vision without jumping ecosystems.
• Robotics & edge-AI tinkerers: On-board acceleration, camera/audio pipelines, and Linux packages (OpenCV, PyTorch-lite style stacks) on a board that still does hard-real-time I/O.
• Startups & SMEs: Faster prototyping → pilot hardware using a parts catalog and partners already aligned with Arduino.

Practical next steps for the community

1. Evaluate UNO Q for your use case. If you’ve hit the ceiling on Cortex-M sketches, the dual-brain approach may simplify your stack. Check RAM/eMMC options (2 GB/16 GB or 4 GB/32 GB).
2. Kick the tires on App Lab. Try deploying a vision or audio model and measure end-to-end friction vs. your current flow.
3. Design for portability. Keep HAL layers clean and prefer standards (POSIX, Zephyr drivers, CMSIS, Arduino Core APIs). If you must use a Qualcomm-specific accelerator, gate it behind feature flags. (General guidance informed by acquisition statements that brand independence and multi-vendor support continue.)
4. Stay plugged into policy updates. Follow Arduino’s blog and the official announcement page for any post-closing changes.

Conclusion

For makers, this looks less like an extinction event and more like a bigger runway: Arduino’s user-friendly on-ramp meets Qualcomm’s horsepower and supply-chain muscle.

If promises around openness and vendor inclusivity hold, the community gains new headroom, especially for edge AI, robotics, and connected devices, without losing the low-friction experience that made Arduino the first board so many of us ever loved.

Sources: GsmaIntelligence.com. TechRadar. The Verge. LED Professional. Arduino Blog. Electronic Design. TechRadar. Reuters. Tom’s Hardware.

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Microsoft just rolled out another wave of Copilot upgrades for Windows 11, voice activation (“Hey Copilot”), deeper on-screen understanding via Copilot Vision, and new “agentic” abilities that can take actions on your behalf. The company’s own headline says it’s “making every Windows 11 PC an AI PC.”

First, what is an “AI PC”?

In Microsoft’s strictest sense, the Copilot+ PC label requires dedicated on-device AI horsepower, an NPU capable of ≥40 TOPS, alongside baseline RAM and storage, so features like Recall, Cocreator, and Auto Super Resolution can run locally, fast, and (in theory) more privately.

What changed with the latest Copilot update?

Three things stand out:

1. Ubiquity. Copilot is now more present and easier to invoke (voice, taskbar integration preview, and broader “Vision” availability), with Microsoft emphasizing AI woven through everyday Windows experiences, it is not just a sidebar chatbot.
2. Actions, not just answers. “Copilot Actions” can perform tasks. For example, making reservations or manipulating files, within permission boundaries you approve.
3. Not only for Copilot+ PCs. Much of this update runs on any Windows 11 machine, which is why Microsoft says “every PC” is becoming an AI PC. Independent coverage underscores that most of the new Copilot capabilities don’t require an NPU.

So, are we there yet?

Well, that very much depends on one’s definition of AI PC.

• If “AI PC” means AI features everywhere, discoverable in the shell and usable by mainstream users: Microsoft’s new update is a real step. Copilot is more proactive, more contextual, and increasingly able to do things, not just chat.
• If “AI PC” means on-device intelligence by default; low latency, private, battery friendly workflows running on your laptop’s NPU, then the Copilot+ definition still matters. Many headline features remain tied to NPU class hardware, and Microsoft’s own requirements haven’t changed.

The catch: fragmentation and fit

• Feature fragmentation. Some experiences (e.g., Recall. You can watch my video on that here) have been controversial and are still gated, optional, or hardware dependent. Organizations and even universities have urged caution; Microsoft now exposes Recall as an optional Windows feature you can enable or remove. Expect IT to curate which “AI” shows up on endpoints.
• Rollout reality. Several integrations (like taskbar level Copilot chat) are starting in Insider previews and may take time to reach everyone.
• Windows versioning. Big AI leaps have aligned with the Windows 11 24H2 platform and later; enterprise rollout pacing and known issues tracking still apply.

What it means for you

• On older PCs: You’ll see more Copilot entry points and useful assistance without buying new hardware, which is good for everyday guidance, light automation, and quick explanations.
• On Copilot+ PCs: You get the fuller vision; local AI effects, creation tools, and (where enabled) time machine style retrieval, thanks to that ≥40 TOPS NPU. This is where the AI-PC promise feels native and not bolted on.

Conclusion

Microsoft’s latest update makes Windows feel much closer to the AI-first experience it has been pitching: Copilot is more capable, more present, and beginning to act on your behalf. But the truest version of an “AI PC” still hinges on on-device NPUs and the Copilot+ class of hardware. In short: AI for everyone now; fully native AI when you buy the right silicon.

Sources: Reuters, Microsoft, Microsoft Learn, Microsoft Support, Windows Blog, Windows Central

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Now that Windows 10 support has ended and buying a new PC is not your highest priority, what are your options?

Ideas And Suggestions

Check out these two videos for some useful information on the subject.

If you would like to share your thoughts, drop them in the Comments section below.

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