#TechBlog

[freshyblog07]

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[gu-ro]

crythedral.neocities.org is live! I created a techblog / neocities page where I can document my projects. I already have some write-ups and will be adding more over time. Intermittently I'll showcase an article here. It's made from scratch with no js so it's a little raw and doesn't like mobile.

[themajortechie]

so. minecraft's latest snapshot supposedly makes it so the game now requires both java 21 and a 64-bit cpu.

HAVE AT YE, MOJANG

haha

i am in great pain.

[not-radioshack]

Writeup: AOpen i945GMm-HL shenanigans

AOpen i945GMm-HL - The Retro Web

Welp. This board is weirder than I ever thought it'd be. Not the board in general, but the specific one I bought.

To begin, it turns out that my particular board, and likely many others of the same model, are OEM-customized boards that AOpen provided to a little company called RM Education. They make all-in-one PCs for the UK market.

...And they are using evaluation BIOSes (in other words, BIOS software that's normally only meant for prototyping and... well, evaluation) in their retail boards.

My specific board contains BIOS version R1.08, which is actually R1.02 apparently. There is evidence of an R1.07 existing as well from a reddit thread on the r/buildapc subreddit, but I doubt that it's been dumped anywhere.

Moving on to the original point of this writeup, I got this board because I wanted to build a system that pushed the 32-bit Core Duo T2700 as far as possible, meaning I needed a mobile-on-desktop board. AOpen built a reputation for doing this sorta stuff in the 2000s, so I went ahead and picked one of their boards for use (although I would've much preferred using the top of the line AOpen i975Xa-YDG instead if it were being sold anywhere. That's a VERY tasty looking board with its full size DIMM slots and SLI-compatible dual PCIe x16 slots and ability to crank the FSB all the way to 305MHz).

Slightly surprisingly, the Core Duo T2700 is quite the overclocker! It's able to push from 2.3GHz all the way up to 2.7GHz with some FSB overclocking using the SetFSB tool. It's multiplier-locked to a range from 6.0 to 14.0, so I can only push it through this means.

The board I'm using, the AOpen i945GMm-HL, supports running the FSB up to 195MHz. It's okay-ish in terms of stability, but crashes when running Aida64 benchmarks unless I loosen the memory timings from the 5-5-5-15 settings that it uses at 333MHz to 5-6-6-18, which is just the tiniest bit faster than its stock settings for 400MHz operation by SPD. With these settings, it's much more stable and is able to run the benchmarks, though unless I lower the FSB from 195MHz to 190, it will consistently crash Chrome when trying to play Youtube videos on integrated graphics. I'll likely experiment some to see if adding a card capable of handling the video playback in hardware helps.

For now, this is all for this blog post. I'll follow-up with more details as they come in reblogs. As follows are the specs of the system:

AOpen i945GMm-HL (OC'ed from 166MHz FSB to 195MHz, 190MHz for more stability)

Intel Core Duo T2700 @ 2.7GHz (OC'ed from 2.3GHz)

2x 2GB Crucial DDR2 SO-DIMMs @ 5-6-6-18 timings

Some random 40GB Hitachi hdd lol

Windows XP Pro SP3, fully updated via LegacyUpdate

Supermium Browser (fork of Google Chrome and the reason why I was able to test Youtube playback in the first place)

Coming up: Installing One-Core-API and Java 21 to play Minecraft 1.21 on a 32-bit system out of spite for Microsoft "dropping support" for 32-bit CPUs.

[digitalworldnext]

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[innovatexblog]

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[allnewtrending]

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[nschool]

Behind the Scenes of Google Maps – The Data Science Powering Real-Time Navigation

Whether you're finding the fastest route to your office or avoiding a traffic jam on your way to dinner, Google Maps is likely your trusted co-pilot. But have you ever stopped to wonder how this app always seems to know the best way to get you where you’re going?

Behind this everyday convenience lies a powerful blend of data science, artificial intelligence, machine learning, and geospatial analysis. In this blog, we’ll take a journey under the hood of Google Maps to explore the technologies that make real-time navigation possible.

The Core Data Pillars of Google Maps

At its heart, Google Maps relies on multiple sources of data:

Satellite Imagery

Street View Data

User-Generated Data (Crowdsourcing)

GPS and Location Data

Third-Party Data Providers (like traffic and transit systems)

All of this data is processed, cleaned, and integrated through complex data pipelines and algorithms to provide real-time insights.

Machine Learning in Route Optimization

One of the most impressive aspects of Google Maps is how it predicts the fastest and most efficient route for your journey. This is achieved using machine learning models trained on:

Historical Traffic Data: How traffic typically behaves at different times of the day.

Real-Time Traffic Conditions: Collected from users currently on the road.

Road Types and Speed Limits: Major highways vs local streets.

Events and Accidents: Derived from user reports and partner data.

These models use regression algorithms and probabilistic forecasting to estimate travel time and suggest alternative routes if necessary. The more people use Maps, the more accurate it becomes—thanks to continuous model retraining.

Real-Time Traffic Predictions: How Does It Work?

Google Maps uses real-time GPS data from millions of devices (anonymized) to monitor how fast vehicles are moving on specific road segments.

If a route that normally takes 10 minutes is suddenly showing delays, the system can:

Update traffic status dynamically (e.g., show red for congestion).

Reroute users automatically if a faster path is available.

Alert users with estimated delays or arrival times.

This process is powered by stream processing systems that analyze data on the fly, updating the app’s traffic layer in real time.

Crowdsourced Data – Powered by You

A big part of Google Maps' accuracy comes from you—the users. Here's how crowdsourcing contributes:

Waze Integration: Google owns Waze, and integrates its crowdsourced traffic reports.

User Reports: You can report accidents, road closures, or speed traps.

Map Edits: Users can suggest edits to business names, locations, or road changes.

All this data is vetted using AI and manual review before being pushed live, creating a community-driven map that evolves constantly.

Street View and Computer Vision

Google Maps' Street View isn’t just for virtual sightseeing. It plays a major role in:

Detecting road signs, lane directions, and building numbers.

Updating maps with the latest visuals.

Powering features like AR navigation (“Live View”) on mobile.

These images are processed using computer vision algorithms that extract information from photos. For example, identifying a “One Way” sign and updating traffic flow logic in the map's backend.

Dynamic Rerouting and ETA Calculation

One of the app’s most helpful features is dynamic rerouting—recalculating your route if traffic builds up unexpectedly.

Behind the scenes, this involves:

Continuous location tracking

Comparing alternative paths using current traffic models

Balancing distance, speed, and risk of delay

ETA (Estimated Time of Arrival) is not just based on distance—it incorporates live conditions, driver behavior, and historical delay trends.

Mapping the World – At Scale

To maintain global accuracy, Google Maps uses:

Satellite Data Refreshes every 1–3 years

Local Contributor Programs in remote regions

AI-Powered Map Generation, where algorithms stitch together raw imagery into usable maps

In fact, Google uses deep learning models to automatically detect new roads and buildings from satellite photos. This accelerates map updates, especially in developing areas where manual updates are slow.

Voice and Search – NLP in Maps

Search functionality in Google Maps is driven by natural language processing (NLP) and contextual awareness.

For example:

Searching “best coffee near me” understands your location and intent.

Voice queries like “navigate to home” trigger saved locations and route planning.

Google Maps uses entity recognition and semantic analysis to interpret your input and return the most relevant results.

Privacy and Anonymization

With so much data collected, privacy is a major concern. Google uses techniques like:

Location anonymization

Data aggregation

Opt-in location sharing

This ensures that while Google can learn traffic patterns, it doesn’t store identifiable travel histories for individual users (unless they opt into Location History features).

The Future: Predictive Navigation and AR

Google Maps is evolving beyond just directions. Here's what's coming next:

Predictive Navigation: Anticipating where you’re going before you enter the destination.

AR Overlays: Augmented reality directions that appear on your camera screen.

Crowd Density Estimates: Helping you avoid crowded buses or busy places.

These features combine AI, IoT, and real-time data science for smarter, more helpful navigation.

Conclusion:

From finding your favorite restaurant to getting you home faster during rush hour, Google Maps is a masterpiece of data science in action. It uses a seamless combination of:

Geospatial data

Machine learning

Real-time analytics

User feedback

…all delivered in seconds through a simple, user-friendly interface.

Next time you reach your destination effortlessly, remember—it’s not just GPS. It’s algorithms, predictions, and billions of data points working together in the background.

[offresdujour]

Summer essential 💨🧊

Je ne sors plus sans mon nouveau mini ventilateur JISULIFE. Pourquoi ? Parce que :

— Il a 100 vitesses réglables (oui, 100 !) — Il est super silencieux — Il tient la journée avec sa batterie 5000mAh — Il est canon avec son design en métal — Et il tient dans la main ou dans le sac. 👝

C’est littéralement mon meilleur achat de cet été. Que ce soit pour les balades, les transports ou juste bosser dehors, il me sauve.

💡 Si vous en cherchez un aussi, voici le lien (affilié, merci pour le soutien 💕) :

[itg4]

Cybersecurity in the Digital Age: Your Digital Fortress Starts Here

🌐 In today’s fast-paced digital environment, where every click, transaction, and interaction happens online, cybersecurity has become the frontline of digital survival. Whether you're a business owner, developer, creator, or consumer, safeguarding your digital assets isn't just a luxury—it's a necessity.

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🔐 Why Cybersecurity Should Be Your Top Digital Priority

The digital ecosystem is expanding—and so are cyber threats. From data breaches to malware attacks, businesses and creatives face evolving risks. That’s why integrating cybersecurity across your website, app, and marketing platforms is now mission-critical.

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From Blogger:

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☁️ Cloud Infrastructure in 2025

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[themajortechie]

A call to help on repairing a one-of-a-kind prototype!

[not-radioshack]

Writeup: Forcing Minecraft to play on a Trident Blade 3D.

The first official companion writeup to a video I've put out!

So. Uh, yeah. Trident Blade 3D. If you've seen the video already, it's... not good. Especially in OpenGL.

Let's kick things off with a quick rundown of the specs of the card, according to AIDA64:

Trident Blade 3D - specs

Year released: 1999

Core: 3Dimage 9880, 0.25um (250nm) manufacturing node, 110MHz

Driver version: 4.12.01.2229

Interface: AGP 2x @ 1x speed (wouldn't go above 1x despite driver and BIOS support)

PCI device ID: 1023-9880 / 1023-9880 (Rev 3A)

Mem clock: 110MHz real/effective

Mem bus/type: 8MB 64-bit SDRAM, 880MB/s bandwidth

ROPs/TMUs/Vertex Shaders/Pixel Shaders/T&L hardware: 1/1/0/0/No

DirectX support: DirectX 6

OpenGL support: - 100% (native) OpenGL 1.1 compliant - 25% (native) OpenGL 1.2 compliant - 0% compliant beyond OpenGL 1.2 - Vendor string:

Vendor : Trident Renderer : Blade 3D Version : 1.1.0

And as for the rest of the system:

Windows 98 SE w/KernelEX 2019 updates installed

ECS K7VTA3 3.x

AMD Athlon XP 1900+ @ 1466MHz

512MB DDR PC3200 (single stick of OCZ OCZ400512P3) 3.0-4-4-8 (CL-RCD-RP-RAS)

Hitachi Travelstar DK23AA-51 4200RPM 5GB HDD

IDK what that CPU cooler is but it does the job pretty well

And now, with specs done and out of the way, my notes!

As mentioned earlier, the Trident Blade 3D is mind-numbingly slow when it comes to OpenGL. As in, to the point where at least natively during actual gameplay (Minecraft, because I can), it is absolutely beaten to a pulp using AltOGL, an OpenGL-to-Direct3D6 "wrapper" that translates OpenGL API calls to DirectX ones.

Normally, it can be expected that performance using the wrapper is about equal to native OpenGL, give or take some fps depending on driver optimization, but this card?

The Blade 3D may as well be better off like the S3 ViRGE by having no OpenGL ICD shipped in any driver release, period.

For the purposes of this writeup, I will stick to a very specific version of Minecraft: in-20091223-1459, the very first version of what would soon become Minecraft's "Indev" phase, though this version notably lacks any survival features and aside from the MD3 models present, is indistinguishable from previous versions of Classic. All settings are at their absolute minimum, and the window size is left at default, with a desktop resolution of 1024x768 and 16-bit color depth.

(Also the 1.5-era launcher I use is incapable of launching anything older than this version anyway)

Though known to be unstable (as seen in the full video), gameplay in Minecraft Classic using AltOGL reaches a steady 15 fps, nearly triple that of the native OpenGL ICD that ships with Trident's drivers the card. AltOGL also is known to often have issues with fog rendering on older cards, and the Blade 3D is no exception... though, I believe it may be far more preferable to have no working fog than... well, whatever the heck the Blade 3D is trying to do with its native ICD.

See for yourself: (don't mind the weirdness at the very beginning. OBS had a couple of hiccups)

Later versions of Minecraft were also tested, where I found that the Trident Blade 3D follows the same, as I call them, "version boundaries" as the SiS 315(E) and the ATi Rage 128, both of which being cards that easily run circles around the Blade 3D.

Version ranges mentioned are inclusive of their endpoints.

Infdev 1.136 (inf-20100627) through Beta b1.5_01 exhibit world-load crashes on both the SiS 315(E) and Trident Blade 3D.

Alpha a1.0.4 through Beta b1.3_01/PC-Gamer demo crash on the title screen due to the animated "falling blocks"-style Minecraft logo on both the ATi Rage 128 and Trident Blade 3D.

All the bugginess of two much better cards, and none of the performance that came with those bugs.

Interestingly, versions even up to and including Minecraft release 1.5.2 are able to launch to the main menu, though by then the already-terrible lag present in all prior versions of the game when run on the Blade 3D make it practically impossible to even press the necessary buttons to load into a world in the first place. Though this card is running in AGP 1x mode, I sincerely doubt that running it at its supposedly-supported 2x mode would bring much if any meaningful performance increase.

Lastly, ClassiCube. ClassiCube is a completely open-source reimplementation of Minecraft Classic in C, which allows it to bypass the overhead normally associated with Java's VM platform. However, this does not grant it any escape from the black hole of performance that is the Trident Blade 3D's OpenGL ICD. Not only this, but oddly, the red and blue color channels appear to be switched by the Blade 3D, resulting in a very strange looking game that chugs along at single-digits. As for the game's DirectX-compatible version, the requirement of DirectX 9 support locks out any chance for the Blade 3D to run ClassiCube with any semblance of performance. Also AltOGL is known to crash ClassiCube so hard that a power cycle is required.

Interestingly, a solid half of the accelerated pixel formats supported by the Blade 3D, according to the utility GLInfo, are "render to bitmap" modes, which I'm told is a "render to texture" feature that normally isn't seen on cards as old as the Blade 3D. Or in fact, at least in my experience, any cards outside of the Blade 3D. I've searched through my saved GLInfo reports across many different cards, only to find each one supporting the usual "render to window" pixel format.

And with that, for now, this is the end of the very first post-video writeup on this blog. Thank you for reading if you've made it this far.

I leave you with this delightfully-crunchy clip of the card's native OpenGL ICD running in 256-color mode, which fixes the rendering problems but... uh, yeah. It's a supported accelerated pixel format, but "accelerated" is a stretch like none other. 32-bit color is supported as well, but it performs about identically to the 8-bit color mode--that is, even worse than 16-bit color performs.

At least it fixes the rendering issues I guess.

[webnameseller]

Data Science With a Human Side

confessionsofadatascientist.com is a powerful blog or content brand for professionals in tech and analytics. https://www.godaddy.com/en-uk/domainsearch/find?domainToCheck=confessionsofadatascientist.com