In this interview, we sit down with Oleg Kabanov to discuss General-Purpose Visual Programming Language "Pipe". Pipe is a paradigm-shifting visual programming language designed to structurally prevent vulnerabilities and make running code independently observable and repairable in real-time without redeployment.
What does General-Purpose Visual Programming Language "Pipe" do? And why is now the time for it to exist?
Pipe is a general-purpose visual programming language that shrinks the attack surface by design – every running block is independently observable and repairable in real-time, without recompilation or redeployment. Unlike text-based languages, entire vulnerability classes are structurally prevented at the language level, making Pipe a natural fit for high-assurance systems and automated self-healing pipelines.
Now’s a good time for General-Purpose Visual Programming Language "Pipe" to exist because AI can now find zero-day vulnerabilities in seconds, meaning that fundamentally reducing the attack surface itself is the only way to win the race against automated threats.
What is your traction to date? How many people does General-Purpose Visual Programming Language "Pipe" reach?
The pipelang.com website and published book reach an estimated several hundred developers and researchers monthly. The cybersecurity article at pipelang.com/threat.html has been shared across developer and security communities. Ten provisional USPTO patents signal active IP development tracked by the broader programming language research community.
Who does your project serve? What’s exciting about your users and customers?
Security-conscious engineering teams in high-assurance industries – defense, finance, critical infrastructure, and healthcare – where structural vulnerability prevention and runtime observability justify adopting a new language. Secondary audience is DevOps and platform engineering teams building automated self-healing pipelines who need direct runtime access to individual application components without redeployment overhead.
What technologies were used in the making of General-Purpose Visual Programming Language "Pipe"? And why did you choose ones most essential to your techstack?
To bring Pipe's Proof of Concept (POC) to life, the team is planning to leverage a modern, multi-platform technology stack. Flutter will be utilized on the frontend to create a highly flexible and responsive visual interface, while Go and Rust are designated for the backend to ensure memory safety, robust performance, and secure concurrent processing.
What is traction to date for General-Purpose Visual Programming Language "Pipe"?
Around the web, who’s been noticing?
Although currently in the pre-launch phase, Pipe has established a strong foundation of credibility through its published book and comprehensive language specification available at pipelang.com. The project has successfully filed 10 provisional USPTO patents and is gaining institutional momentum, having been selected for the CodeLaunch Canada 2026 cohort, which will culminate in an investor showcase in September 2026.
General-Purpose Visual Programming Language "Pipe" scored an 86 proof of usefulness score (https://proofofusefulness.com/report/general-purpose-visual-programming-language-pipe) – how do you feel about that? Needs reassessed or just right?
Looking at the criteria, I think it's about right, and I'd rather say that directly than argue with it. Real-World Utility and Technical Innovation are where this project should score reasonably well – the problem is real and the architecture is deeply specified. But Evidence of Traction and Audience Reach together make up nearly half the algorithm's weight, and that's exactly where Pipe is currently thin: there's no live product, no users, and no revenue yet. A score sitting just inside the lowest positive tier is an accurate reflection of that gap, not a misjudgment of the underlying idea.
What I'd push back on isn't the number – it's the implication that this is a static result. The plan over the coming months is specifically aimed at the two heaviest-weighted criteria: a narrow working executor to generate the first real traction signal, and continued visibility through publication and outreach to build audience reach on top of what the recent article already started. If the score gets re-run once that exists, I'd expect it to move meaningfully – not because the project changed, but because there'd finally be something to measure against the criteria that currently have nothing to point to.
What excites you about this project's potential usefulness?
Here's the problem no one is solving at the root: AI can now find zero-day vulnerabilities in seconds. Two independent researchers found the same massive Linux kernel exploit within nine hours of each other. Every third-party library in a production app is attack surface continuously scanned by AI. Patching faster and hiding source code both lose the same race. The real answer is reducing the attack surface itself.
Visual programming language Pipe provides the highest level of granularity without the overhead that text-based architectures impose. Every block is independently accessible in real-time – meaning any compromised or vulnerable component can be isolated, fixed, or replaced directly on production, before a new version is redeployed. No recompilation, no deployment pipeline wait. The attack surface shrinks because the architecture makes every piece of it visible, accessible, and replaceable.
Telemetry can be acquired from any point in the application by simply connecting to a visual block in real time, directly in production – eliminating the need to modify and redeploy code with added logs. This allows deep and comprehensive monitoring of visual application at every point without a need in any embedded log statements.
Pipe is also a natural fit for software self-healing systems. Every block is independently accessible down to individual blocks, so a self-healing engine can pinpoint and fix the exact problem precisely, instantly and without recompilation – while the proper fix goes through the normal pipeline.
Full argument: hackernoon.com/why-the-next-programming-paradigm-has-to-be-visual
Detailed security analysis: pipelang.com/threat.html
Full language spec and demo: pipelang.com
Walk us through your most concrete evidence of usefulness. Not vanity metrics or projections – what's the one data point that proves people genuinely need what you've built?
The clearest data point isn't usage of Pipe itself – there's no live product yet, so there's nothing to generate that kind of evidence. It's that the urgency Pipe is built around just stopped being my own claim and became official multi-government consensus. On June 22, 2026, the US, UK, Canadian, Australian, and New Zealand cyber and intelligence agencies (CISA, NSA, GCHQ's NCSC, Australia's ACSC, Canada's Cyber Centre, NZ's NCSC) issued a joint statement saying frontier AI is shifting the cyber risk landscape on a timeline measured "in months, not years." That's the exact thesis behind Pipe's "Security Armageddon" framing, now stated by the people whose job is assessing this risk at a national level, not by me.
I want to be precise about what this does and doesn't prove. It's strong evidence the problem is real and urgent – not evidence that Pipe specifically is the right fix. The agencies' own recommended response is faster patching and AI-augmented defense within existing systems, which is a different approach than what Pipe proposes. What this data point demonstrates is that the gap I'm trying to close isn't speculative or self-serving – it's the same gap six national cybersecurity agencies are telling the world to take seriously, right now.
How do you measure genuine user adoption versus "tourists" who sign up but never return? What's your retention story?
Honestly, I don't have a retention story yet, because there's no live signup product to measure it against – the IDE is still at the canvas-and-elements stage, not a released beta.
I'd rather say that plainly than back into a metric that doesn't mean anything yet. Once the beta opens, the plan is to track weekly active builders against signups from day one, specifically because I want real adoption data, not signup vanity numbers, driving the next round of decisions.
If we re-score your project in 12 months, which criterion will show the biggest improvement, and what are you doing right now to make that happen?
Traction and proof-of-functionality, by a wide margin. Right now Pipe is strong on specification depth and weak on demonstrated execution - anyone evaluating it correctly should weight that gap heavily. What I'm doing right now is building a narrow, scoped executor: not the full IDE, just enough to show a live diagram actually running and being modified in place without a rebuild. That single artifact is the thing every other piece of traction – funding conversations, technical credibility, developer interest – is currently waiting on.
How Did You Hear About HackerNoon? Share With Us About Your Experience With HackerNoon.
I came to HackerNoon through their developer/tech publishing reputation while looking for the right venue to make a substantive technical argument rather than a press-release-style announcement. The experience has been genuinely good – the piece was boosted and selected for the Decentralize AI Hackathon, and the platform clearly reaches readers who engage with the actual argument rather than skimming past it, which is exactly what I needed for something this early-stage and unproven.
With your upcoming CodeLaunch Canada 2026 investor showcase in September, what are the key traction milestones you hope to achieve between now and the event?
Between now and September, the realistic goals are: a working narrow executor demonstrating live, in-place modification of a running diagram; continued progress converting the 10 provisional patents and growing early community engagement around the specification and the published article. I'm deliberately not promising paying customers or large user numbers by then – the honest milestone is moving from "fully specified but unproven" to "specified and demonstrably running," which is the gap that matters most right now
Transitioning from a pre-launch concept to an active platform is a big leap; how do you plan to drive organic growth and user adoption among high-assurance engineering teams once your POC is complete?
High-assurance engineering teams are a narrower, more skeptical, more technically literate audience than general developers, so the plan is to meet them where their own trust signals already live rather than running generic developer marketing at them: engaging directly in the formal-methods and safety-critical-systems communities that already use tools like AADL and SCADE, publishing technical comparisons that are honest about where Pipe is and isn't proven yet, and pursuing design partnerships with one or two such teams early, where credibility transfers by direct collaboration rather than by broadcast.
Pipe promises to structurally prevent entire vulnerability classes and enable real-time observability—what will be the most significant immediate benefit or usefulness for a DevOps team implementing Pipe for their self-healing pipelines?
The immediate benefit is collapsing the gap between detecting a problem and fixing it. Today, even a fast detection still has to go through a full rebuild-and-redeploy cycle before a fix takes effect – that cycle is the actual window an attacker or a cascading failure exploits. With Pipe, a DevOps team or an automated self-healing engine can isolate, patch, or reroute the specific affected block directly on the running system, with the fix taking effect immediately, no redeployment in between.