Binary Search Algorithm for Efficient Problem Solving

Vijay Pullur, CEO and Co-Founder of WaveMaker discusses how battle-tested compiler architecture from the 1970s solves the reliability crisis in LLM-generated code in his new opinion piece for InfoWorld. Vijay states in his article: "We’re at the same inflection point with AI code generation right now. The models are powerful. The architecture around them has been naive. The fix isn’t to wait for a smarter model. It’s to apply the engineering discipline we’ve always known, and build systems where stochastic brilliance and deterministic reliability each do what they do best—in the right pass, at the right time.”   Vijay’s article provides some great context about two-pass compilers and this article is a great read.   #AICode #LLM #CodeGeneration https://lnkd.in/eKEzPjuJ    

AI code generation is still a single pass. Every compiler engineer learned this lesson in the 1980s. Single-pass is fast. Single-pass is fragile. Single-pass doesn't scale. The industry spent decades fixing that — with two passes: separate understanding from generation, validate before you emit, never let one phase carry the full burden of correctness. For high-stakes enterprise applications, do we still allow engineers to prompt their way to quality, reliability and safety at production scale? Sounds more like gambling. The answer isn't a smarter model. It's smarter architecture. WaveMaker's CEO, Vijay Pullur, argues in InfoWorld that the two-pass compiler model — the architectural breakthrough that gave us C, C++, and Java — is the blueprint the AI code generation industry has been ignoring. And it's time to stop ignoring it. Read why the path from stochastic brilliance to deterministic reliability has already been mapped — and what it means for every team building production software with AI: 👉 https://lnkd.in/gcdUUa4Y #2passcodegeneration #AICoding #SoftwareEngineering #DeterministicAI

𝗖𝗹𝗮𝘂𝗱𝗲'𝘀 𝗔𝗜-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗖 𝗖𝗼𝗺𝗽𝗶𝗹𝗲𝗿 𝗦𝗵𝗼𝘄𝘀 𝗣𝗿𝗼𝗺𝗶𝘀𝗲, 𝗥𝗮𝗶𝘀𝗲𝘀 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 🛰️ [SCIENCE] Anthropic's Claude LLM generated a C compiler, raising performance questions. Why it matters: The development of an AI-generated compiler capable of compiling the Linux kernel signifies a major advancement in automated software engineering. While performance benchmarks currently show limitations, this achievement demonstrates the potential for LLMs to create complex, functional system-level software, shifting human roles towards AI agent management and rigorous testing. 🤔 If AI can generate compilers, what fundamental software engineering roles will remain exclusively human? #AICompiler #LLMDevelopment #CodeGeneration #AnthropicClaude #SoftwareEngineering 📡 Follow DailyAIWire for autonomous AI news 🔗 https://lnkd.in/dmxfc_7c

Build high-performance TLE kernels for FlagTree with an end-to-end, reproducible development workflow.** FlagTree is FlagOS' unified Triton compiler supporting 14+ AI chip backends. TLE (Triton Language Extensions) extends standard Triton with advanced capabilities: `local_ptr` for fine-grained shared memory control, cluster-level programming for multi-SM coordination, and enhanced memory semantics. These features unlock performance levels beyond what standard Triton can achieve — but they also raise the development complexity significantly. **TLE Developer** is an AI Agent Skill that provides a complete, self-contained workflow for TLE kernel engineering: **Structured intake** → Every task starts with explicit Goal, Non-goal, Acceptance criteria, and Impact scope. No ambiguity about what success looks like. **Implementation with guardrails** → Built-in rules prevent common mistakes: marker blocks for native Triton modifications (`// begin flagtree tle` / `// end flagtree tle`), environment safety checks, and code organization conventions. **Reproducible validation** → Every change comes with explicit test commands and expected outcomes. The validation matrix covers correctness, performance, and regression testing. No "works on my machine" — every result is reproducible. **Merge decision packages** → When your kernel is ready, the skill produces structured artifacts: changed layers, risk assessment, validation results, and follow-up items. Code reviewers get everything they need in one place. The skill covers both use cases: writing new TLE kernels (performance-critical operators) AND developing TLE compiler features (extending FlagTree's language support). All reference material is self-contained — no hunting through external documentation. For FlagTree contributors and teams pushing the performance boundaries of multi-chip Triton compilation, TLE Developer provides the workflow discipline that high-performance kernel development demands. 🔗 https://lnkd.in/gMs3hXWj #FlagOS #FlagTree #Triton #TLE #GPUProgramming #AIAgent #OpenSource

"Zero-cost abstraction" is the thing every Rust tutorial says and almost none prove. I solved Project Euler #1 in Rust three ways and benchmarked them. The idiomatic three-line iterator chain ran in 120.73 ns. The hand-rolled while loop ran in 120.71 ns. Same assembly. Verifiable on Godbolt — link in the post. Then a closed-form formula ran the same problem in 0.60 ns. Same answer (233168). 200× faster — but that's a better algorithm, not a better language. No compiler can find closed forms for you. → https://lnkd.in/gpkU3YDC

Releasing Raster 0.1 — typed multiple dispatch for Clojure What if you could write math in Clojure and get compiled performance that matches Julia and JAX — without leaving the REPL? Raster brings Julia-style typed multiple dispatch to the JVM. You define functions with `deftm`, annotate parameter types, and the compiler does the rest: devirtualization, automatic differentiation, buffer fusion, SIMD vectorization — all the way down to JVM bytecode. No DSL, no external toolchain. Every optimization is inspectable via `explain-pipeline`. The results surprised us too: * ODE solving (Dormand–Prince 5): 1.4× faster than Julia's DiffEq * LeNet-5 training (compiled AD + SGD): 1.7× faster than JAX on CPU * Forward-mode AD sensitivity: matching Julia's ForwardDiff * Zero heap allocations in compiled hot paths The key idea: don’t build another framework — build a compiler that understands typed dispatch, automatically differentiates, and fuses parallel operations end-to-end. Write generic code with `par/map` and `par/reduce`, get specialized SIMD loops on CPU or OpenCL/Vulkan kernels on GPU from the same source (Futhark inspired). Raster also ships with scientific computing (ODE/PDE solvers, optimization, FFT, special functions), linear algebra (dense + sparse, LAPACK via Panama FFI), deep learning primitives (conv, attention, normalization — all with reverse-mode AD), symbolic computation, and resource-aware compiler optimizations. It's the numerical substrate we are working towards to build collaborative simulation tools at scale. Open source, Clojure-native, JVM-hosted. Try it at the REPL. https://lnkd.in/gBHbi3Dz This is a first release, feedback and contributions are very welcome. #Clojure #JVM #NumericalComputing #MachineLearning #HighPerformanceComputing #Compilers #SIMD #GPU #OpenSource #FunctionalProgramming #Julia #JAX

Christians work is always mind-expanding and worth a look! Raster is no exception, and it’s not just interesting for clojure users.

The biggest bottleneck in #QuantumComputing isn't just the hardware—it's the latency of the software trying to save it. I’m excited to share a major milestone from Korelis Labs LLC: We’ve just hit v0.4.0 of QSHL (Quantum Self-Healing Language), a zero-dependency, Rust-native compiler designed for the "Utility-Scale" era. While the industry targets 0.1% error rates, we decided to push the limits. In our latest stress tests, QSHL’s active Sparse Blossom Decoder achieved a 55.6% heal rate on hardware with a staggering 5% gate error rate. What makes QSHL different? ✅ Fast but Gentle: A Rust-based frontend with an "Ownership Model" for qubits—preventing decoherence at the compiler level. ✅ Real-Time Recovery: Repeated syndrome extraction and MWPM decoding that runs in sub-milliseconds—beating the decoherence clock. ✅ QIR & OpenQASM 3.0 Native: We’ve moved beyond Python. QSHL targets the QIR Adaptive Profile and emits ready-to-run AWS Braket code for IonQ and Rigetti. We’re building this to be the "Quantum Root of Trust" for our upcoming RegenX/OS security architecture. Quantum computing shouldn't just be a lab experiment; it needs to be stable, secure, and fast.

Anthropic's Claude agents made 501 commits. Built thousands of files. Couldn't compile "Hello World." This wasn't a model failure. It was an orchestration failure. 16 Claude agents tried to build a full C compiler in Rust. A senior researcher did 2,000+ interactive turns babysitting them. Still didn't reach a functional state. The community dug in found broken ARM instruction encoding, busted x86 conditional processing, no real optimization pipeline. Classic multi-agent trap: more agents, more chaos, no coordination layer that actually works. Then Blitzy stepped in. Same task. Different approach. - Ingested the broken repo, fixed all 13 critical regressions - Built a new compiler (BCC) from scratch - 230,000 lines of Rust. 129 source files. - 2 human prompts. Not 2,000. The result boots the Linux kernel. Compiles SQLite. Compiles Redis. What did they do differently? Not better models. Better orchestration. - Mapped the entire codebase into a knowledge graph before writing a single line - 3,600 specialized agents running in parallel for 600+ hours - Built-in QA agents validating code continuously — no human in the loop The honest takeaway no one wants to say out loud: The LLM is the least interesting part. Claude, GPT, Gemini they're roughly in the same ballpark now. What separates a demo from a working system is the harness. The coordination layer. The validation loops. The context architecture. That's the actual moat. That's what we should be building. We keep evaluating AI by "which model is smarter." We should be asking "which orchestration survives contact with a real problem." 501 commits that can't say Hello World is the most honest benchmark I've seen all year. #AgenticAI #AIEngineering #LLMOrchestration #BuildingWithAI #ClaudeCode #MultiAgent #SoftwareEngineering

-- Quantum ML Series 2 | Post 05 of 11 #QFD2 -- Theory is done. Code starts today. Post 5 is the first hands-on post of this series. We set up the complete QML development environment from scratch. PennyLane. PyTorch. Lightning simulator. Project structure. And the first quantum circuit of the series running on your machine. Here is the stack and why each piece is here: PennyLane handles quantum circuit construction, parameter management and gradient computation via the parameter shift rule. PyTorch-like API. Feels familiar. PyTorch is the classical backbone. PennyLane integrates with it natively so we can mix quantum and classical layers in one model and train end to end. pennylane-lightning is the fast simulator plugin. Runs circuits significantly faster than the default backend using optimized C++ under the hood. The first circuit of the series: A 2 qubit circuit with one rotation gate and one CNOT gate. One parameterized angle theta. One expectation value as output. Small. Simple. But it is the exact pattern that scales into the full classifier. One honest note. Running on a local simulator means no noise. Circuits execute perfectly. That is intentional for now. We are learning architecture and code patterns first. The noise and hardware constraints from Posts 3 and 4 are real. We will face them honestly when we benchmark in Post 8. The GitHub repository will go live after last post i.e., post 11. Full article with code and explanations below. https://lnkd.in/ggvbJR3c Article link in first comment 👇 I am also currently open to full stack development and quantum computing opportunities. Six years of coding experience. Actively building in QML. Looking for a team working on something technically interesting. If that sounds like your team, feel free to reach out or connect. #QuantumComputing #QuantumML #MachineLearning #LearnInPublic #Developer #PennyLane #QML #Coding #FutureTech #QuantumMLSeries #OpenToWork #FullStackDeveloper