The best sources for anyone who wants to drive fast traffic [forget search engines]

For years I tried, in the wrong way, to drive traffic to my small tools — and failed repeatedly. The mistake was putting myself first: I would show up on platforms already firing off links to my site, without first participating, contributing, or building a solid presence within those communities. And believe me: this is not hard at all. You just need patience and a willingness to study what kinds of posts perform best on each site. The second important thing: don’t share your links on serious sites if your content still doesn’t appeal to anyone besides yourself. Seek opinions from people outside your circle — or write about what a lot of people are already wanting to read within your niche. Google Trends is a great tool for that. Also put effort into your profile. A well-written, attractive bio with your site link makes a real difference — and it even helps you get found by search engines. Finally, comment and vote on other users’ posts. Look for people publishing about topics that interest you and where you have some knowledge, and join those conversations. Even short comments are worth it. It was by making these corrections that I started seeing real growth in traffic to my tools. Give it a try and see if you get good results too. Below, I list the sites I’ve been using that have given me the best returns — organized from the ones that drive the most traffic to the least, in my experience. 1. [Hacker News](https://news.ycombinator.com) 2. [X](https://x.com) 3. [Blue sky](https://bsky.app/) 4. [Linkedin](https://linkedin.com) 5. [Threads](https://www.threads.com/) 6. [Facebook](https://facebook.com) 7. [Gab](https://gab.com) 8. [Mastodon](https://mastodon.social/home) 9. [Pinterest](https://pinterest.com) Come tell us if you use this method and get results — we’d love to hear about it!

How Artificial Intelligence Will Die — and What Comes After | Comuniq

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The Secret Guide Every Developer Should Read Backwards

There's a classic programming text called *"How To Write Unmaintainable Code"*, written by Roedy Green in the 90s. The premise is brilliant: using sharp irony, he teaches how to write code so chaotic that only the original author can maintain it. The goal, he claims, was to guarantee lifetime employment. But read in reverse, the text is one of the best practical guides to software best practices ever written. Here's what it actually teaches, beneath the sarcasm: **On naming variables and functions** He suggests using names without vowels, random abbreviations, and variables that differ by just one character, like `swimmer` and `swimner`. The real message: a good name is one you read once and immediately understand. Clarity isn't a luxury, it's respect for whoever comes after you, and that person is often yourself, six months later. **On comments** The ironic tip is to comment the obvious (`/* adds 1 to i */`) and never document what actually matters: the overall purpose of a method, the units of measure for variables, edge cases. A good comment doesn't describe the *what*. It describes the *why*. **On code duplication** He recommends copy, paste, and modify instead of creating reusable modules. It saves time now and guarantees that any future change needs to be made in 25 different places, with no map. No duplication is harmless. **On exceptions and errors** The sarcastic suggestion is to skip error handling because "well-written code never fails." Anyone who has ever had to trace a production bug with no useful logs knows the price of that philosophy. **On the bigger picture** The most serious passage in the text appears near the end. Roedy points out that programming languages are designed by the people who write compilers, but maintained by thousands of other developers who never have a voice in the design. There's a real tension between theoretical elegance and day-to-day practicality. The solution isn't to accept chaos, it's to adopt conventions that make code readable to humans, not just machines. **What to take away from all this** Code is communication. You write it once, but other people read it dozens of times. Every shortcut you take today becomes technical debt tomorrow. And the worst part of technical debt isn't the time it steals: it's the trust it erodes, both in the codebase and in the team. Roedy's text is over 25 years old and remains unsettlingly relevant. The full read is worth it. --- *Source: [How To Write Unmaintainable Code](https://www.doc.ic.ac.uk/~susan/475/unmain.html), Roedy Green*

Oracle lays off thousands… and hires abroad at the same time

Tech giant Oracle is carrying out a wave of layoffs — workers are receiving letters saying "today is your last working day" — but at the same time, the company has submitted over 3,100 H-1B visa petitions in the last two fiscal years to bring specialized foreign workers into the US. <br>The H-1B program is often defended as a way to fill talent gaps. Critics, however, argue it's used to replace local workers with cheaper labor. <br>This case raises an increasingly relevant question in the tech industry: when a company cuts jobs and requests foreign work visas at the same time, what's the real story? <br>What do you think? A legitimate talent strategy or a worrying sign for the tech job market? Source: https://nationaltoday.com/us/tx/austin/news/2026/04/03/oracle-files-thousands-of-h-1b-visa-petitions-amid-mass-layoffs/

One of Apple’s First Employees Looks Back at 50 Years

How Artificial Intelligence Will Die — and What Comes After | Comuniq

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How Artificial Intelligence Will Die — and What Comes After | Comuniq

Join Comuniq to share and explore ideas on technology, science, art, and more.

Agentic AI in Practice: Speed vs. Quality in Code

Garry Tan, CEO of Y Combinator, one of the most influential startup accelerators in the world, sparked a major debate on social media this week after sharing a striking milestone on X: he and his AI coding agents had been deploying 37,000 lines of code per day across five separate projects, on a 72-day consecutive shipping streak. The post went viral quickly. But two days later, a Polish senior software engineer known as Gregorein decided to take a closer look at the actual results, and what he found was quite revealing: Tan's code was full of bloat, waste, and rookie mistakes, even on the public-facing side of the site. **What does this teach us?** The core of the debate is that while AI coding tools make it easy to pump out lots of code, it is really the quality of the code that matters, not the quantity. Code that goes into production without proper scrutiny and testing can cause obvious functional failures, create security vulnerabilities, or introduce issues that surface later and force engineers to track down and fix the underlying problems. As Gregorein put it: "Right now we are in a moment where AI lets you generate code faster than any human can review it, and the answer from people like Garry seems to be 'so stop reviewing'." **The bigger picture: agentic AI in the startup ecosystem** This episode is not isolated. Tan has been a vocal proponent of agentic AI in the startup world. According to him, about 25% of the current YC batch have 95% of their code written by AI, and companies are reaching up to $10 million in revenue with teams of fewer than 10 people. Yet Tan himself acknowledges that human agency and judgment remain irreplaceable. In his own words, "agency and taste are super, super important and humans are going to be a really irreplaceable piece of that." **The real opportunity for those building with AI** Tan also points out that the biggest mistake founders are making today is piling into the saturated coding agent space, which already dominates nearly 50% of all agentic AI activity. The real opportunity lies in the verticals that have barely been touched: healthcare at 1%, legal at 0.9%, education at 1.8%, where AI agents have enormous transformative potential but almost no penetration yet. **What does this mean for IT and technology professionals?** Agentic AI is real and powerful, but it does not replace architecture, code review, and sound engineering practices. The speed at which code can now be generated has already outpaced the human ability to review it. The challenge now is to build quality processes that match this new pace. The biggest open spaces in AI are not in more tools for developers, but in the sectors that have barely been touched. The question is not whether we will use AI to develop software. It is how we will use it responsibly and with sound judgment. --- Source: Fast Company, "Y Combinator's CEO says he ships 37,000 lines of AI code per day. A developer looked under the hood" https://www.fastcompany.com/91520702/y-combinator-garry-tan-agentic-ai-social-media

GDDRHammer and GeForge: When Your GPU Becomes a Backdoor for Hackers

If you think your system's security depends only on what happens inside the CPU, the latest research has some pretty bad news: **GPUs have now firmly entered the realm of serious vulnerabilities**. Researchers have unveiled two new attacks based on the **Rowhammer** technique that can, starting from GPU memory, achieve **complete control of the machine**, including unrestricted access to the main processor's RAM. The attacks are called **GDDRHammer** and **GeForge**, and they work against **Nvidia Ampere** cards such as the RTX 3060 and RTX 6000. --- ## So, What Exactly Is Rowhammer? The Rowhammer technique was first demonstrated in 2014: by repeatedly and rapidly accessing rows of DRAM memory, it is possible to create electrical interference that causes bits in neighboring rows to "flip" from 0 to 1 or vice versa. It sounds like science fiction, but it's pure physics: modern memory is so densely packed that circuits start to "bleed" into each other. Over the past decade, dozens of Rowhammer variants have been developed, eventually enabling attacks over local networks, rooting Android devices, and even stealing 2048-bit encryption keys. Until now, Rowhammer was mostly a CPU and DDR memory problem. That has officially changed. --- ## What's New With GDDRHammer and GeForge Researchers introduced two new exploits — GDDRHammer and GeForge — that work successfully against Ampere-architecture GPUs such as the RTX 3060 and the professional RTX 6000. Using memory massaging techniques, the attacks bypass protections in Nvidia's drivers, steering page tables toward unprotected memory regions. The numbers speak for themselves: - **GDDRHammer** generates an average of 129 bit flips per memory bank on the RTX 6000, a 64-fold increase compared to attacks documented the previous year. - **GeForge** proved even more destructive: it induced 1,171 bit flips on the RTX 3060 and 202 on the RTX 6000. But the raw number of bit flips isn't the scariest part. What comes next is. --- ## How They Achieve Full Control of the Machine The core breakthrough lies in the ability to tamper with the GPU's page table mappings. Researchers modify page table entries via bit flips to gain arbitrary read and write access to GPU video memory, then redirect pointers to CPU memory, ultimately achieving full control over the host's physical memory. In plain terms: a process running on the GPU can escalate its privileges until it effectively owns the entire machine. GeForge goes even further — it can enable unprivileged users to obtain a root shell, granting the highest level of administrative access to the system. --- ## Why This Is Especially Alarming in Cloud Environments The high cost of high-performance GPUs, typically $8,000 or more, means they are frequently shared among dozens of users in cloud environments. This means a malicious user in a multi-tenant setup could use these attacks to compromise not only their own data, but that of every other tenant on the same server. The researchers caution that cloud providers should reassess GPU memory protections as GPU-driven Rowhammer threats continue to evolve. --- ## What Nvidia Recommends Nvidia had already issued guidance following earlier discoveries, and for now **has not released a specific firmware or driver fix** for these new attacks. The recommendations remain: - **Enable ECC (Error-Correcting Code)** at the system level, which adds redundant bits to preserve data integrity - **Enable IOMMU** in the system BIOS, which prevents the GPU from accessing restricted host memory regions The catch? ECC can introduce up to a 10% slowdown for machine learning inference workloads and also reduces available memory capacity by 6.25%. Security comes at a performance cost. And some Rowhammer variants can still bypass ECC protections. --- ## The Takeaway Rowhammer attacks have long been seen as too sophisticated for real-world exploitation. GDDRHammer and GeForge show that's changing: the line between academic research and a usable exploit is getting thinner by the day. For anyone managing environments with shared GPUs, whether in the cloud or in an on-premise data center, the message is clear: **review your ECC and IOMMU settings now**, don't wait for an incident. The GPU is no longer just a processing unit. It is, now, an attack surface too. --- *Source: [Ars Technica, April 3, 2026](https://arstechnica.com/security/2026/04/new-rowhammer-attacks-give-complete-control-of-machines-running-nvidia-gpus/)* don't forget to [sign up](https://chat-to.dev/login) and join our community

How Microsoft Nearly Lost a Trillion Dollars From the Inside

*A senior Azure engineer exposes the behind-the-scenes story of one of the most silent and costly crises in recent cloud computing history.* --- When Axel Rietschin arrived at Microsoft's headquarters in Redmond on the morning of May 1st, 2023, he was anything but a newcomer. He had spent years making direct contributions to the technologies underpinning Azure, with stints on the Windows team, SharePoint Online, and Core OS, where he helped invent the container platform that powers Docker, Kubernetes, and Windows Sandbox. What he did not expect was to find an entire organization planning the impossible as if it were routine. --- ## The First Day That Revealed Everything Rietschin had barely arrived when he was invited to a monthly planning meeting. In the room were leads, architects, and senior engineers. On the screen, a slide packed with familiar acronyms like COM, WMI, VHDX, and ETW, all connected by arrows in a tangle that was difficult to parse. What was being presented was a plan to port that entire stack of Windows components onto the Overlake chip, a tiny fanless ARM SoC the size of a fingernail, designed to consume as little power and memory as possible. A chip where the hardware engineers had reserved just 4KB of dual-ported FPGA memory for communication protocols. Rietschin knew the hardware inside out. He knew the idea was unworkable. But what surprised him most was not the proposal itself. It was the seriousness with which it was received. Nobody in the room questioned it. A Principal Engineering Manager suggested having "a couple of junior developers look into it." --- ## 173 Agents and No Explanation In the days that followed, Rietschin deepened his understanding of the environment. One of the most unsettling discoveries came from a conversation with the head of Microsoft's Linux group: there were 173 software agents identified as candidates to run inside the Overlake chip. For context, Azure at its core sells virtual machines, networking, and storage. With observability and servicing on top, that should require a small number of well-defined central processes. How they arrived at 173 is something that, according to Rietschin himself, will probably never be fully explained. Nobody at Microsoft could articulate what all those agents did, why they existed, or how they interacted with one another. But the problem goes beyond organizational confusion. Those agents were what orchestrated the virtual machines running OpenAI's systems, SharePoint Online, United States government clouds, and other mission-critical infrastructure. A failure there is not just a bug. Depending on the context, it is a collapse with national security implications. --- ## The Real Cost of Technical Complacency The software stack Rietschin encountered was hitting its limits at just a few dozen VMs per node, in an environment where the hypervisor was capable of supporting over a thousand. On top of that, it was consuming enough host server resources to cause noticeable instability in customer VMs, the so-called "noisy neighbor" problem. All of this was happening while Microsoft was in the middle of a historic bet on OpenAI, providing the infrastructure for the most widely used language models in the world. The fragility was not just technical. It was strategic, financial, and at certain moments, a matter of institutional trust. Rietschin says he tried to alert leadership, including the CEO, the Microsoft board, and senior executives in the Cloud and AI division. The silence he received in return is a central part of the story he is telling across a series of articles published on Substack. --- ## What This Means for Azure Users The most important revelation for any company or developer relying on Azure is not Microsoft's internal drama. It is the realization that critical infrastructure can be held together by systems nobody fully understands, planned by teams that had lost touch with the technical reality of what they were building. Rietschin is not saying Azure is insecure today. He is saying that for a considerable period, decisions were made with an alarming distance from real engineering, and that the consequences of that disconnect are still unfolding. The series continues. The near-loss of OpenAI as a customer, the letters sent to the CEO, the incidents involving the US government, and the features promised publicly before the work had even begun are all coming in the next chapters. Worth following. --- **Source:** [How Microsoft Vaporized a Trillion Dollars](https://isolveproblems.substack.com/p/how-microsoft-vaporized-a-trillion)