Lab tests vs real-world vibration conditions: a gap in validation strategy

More maintainers could benefit from having actionable vibration data. Predictive data saves downtime. Catching a problem before it causes a critical failure saves lives.

How often do you test your test stand (or test rig)? RDI Technologies can validate in a 3 second video. 3-Second Recordings Instant Visualization Non-Contact Advantage

Have you ever run an agent for 9 hours and it forgot a critical constraint from hour one? You've tried RAG, you've tried complex memory system - but the truth is, no amount of workarounds can fix the context limitations of today's transformer based models. Until now.

Stop fine-tuning entire models for small problems. It’s wasteful. Most teams try to adapt LLMs by retraining everything. It’s slow, expensive, and hard to maintain. LoRA fixes that. LoRA (Low-Rank Adaptation) updates a small set of parameters instead of the full model. The base model stays frozen. You train lightweight adapters on top. This means: * Faster training * Lower compute cost * Easier deployment And you still get strong task-specific performance. If your use case needs customization without burning resources, this is the approach that actually works. Big models don’t need big changes. They need smarter updates. Stop over-engineering fine-tuning. Start building with LoRA. #Day72 of documenting my learnings and building meaningful connections on LinkedIn.

If you're building with LLMs, here's what you can do with Catalyst. We put together a 5-part video series showing how to go from raw LLM traffic to shipping your own production-ready models: - Plug in observability to track cost, latency, errors, and individual LLM calls - Turn production traffic into structured, usable datasets - Run evals across any model (including your own) - Train custom models that are faster, cheaper, and more accurate for your use case - Deploy to dedicated GPU instances for production Watch the full series: https://lnkd.in/g_tPdBFe

𝗖𝗧𝗦 𝘀𝗸𝗲𝘄 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝗹𝗼𝗼𝗸𝗲𝗱 𝗯𝗲𝗮𝘂𝘁𝗶𝗳𝘂𝗹. 𝗧𝗵𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘂𝗻𝗱𝗲𝗿𝗻𝗲𝗮𝘁𝗵 𝘄𝗮𝘀 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗼 𝗯𝗿𝗲𝗮𝗸 𝗮𝗽𝗮𝗿𝘁. 𝗖𝗧𝗦 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝘆 (𝟮/𝟰): The next morning, my team focused entirely on one question: Why was CTS improving timing numbers while simultaneously making the backend physically less stable? That investigation uncovered the first major issue inside the clock strategy. After nearly 2 days debugging that 28nm block, we realized CTS was over-optimizing skew so aggressively that the backend had started fighting itself. At first glance, the clock reports looked impressive. Skew numbers were extremely tight. Setup closure looked better after every CTS rerun. But one of my engineers noticed something subtle: Buffer growth was accelerating disproportionately in the exact same regions every cycle. Especially around already congested macro boundaries. CTS kept chasing tiny picosecond-level skew improvements that had almost no meaningful silicon benefit anymore. But physically, the cost underneath was huge: • excessive buffering • localized routing congestion • difficult hold behavior • unstable ECO regions • detoured signal routing around clock-heavy areas The backend was technically “improving.” But physically becoming less stable every iteration. That distinction matters more than most teams realize. Because timing convergence and physical convergence are not always the same thing. We decided to slightly relax skew targets. Nothing dramatic. No major flow overhaul. Just enough to stop CTS from aggressively optimizing low-value skew improvements. The impact showed up almost immediately in the next implementation cycle: • routing pressure reduced • buffer growth stabilized • hold behavior became more predictable • runtime started dropping again That was the first moment the backend stopped amplifying its own instability. At that point, timing behavior finally started improving. But one thing still bothered us. CTS was still overcompensating in the exact same physical regions no matter how much skew behavior improved. That was the clue that the deeper issue was not timing anymore. Something underneath the implementation itself was still fighting the clock tree. I’ll break down that part of the recovery in tomorrow’s post.

He shared a lengthy “custom prompt” on X and essentially showed he has little idea how the technology actually works. Read more: https://lnkd.in/gjAyD5s3

MultiCut is typically used to prepare multiple locations on a single sample. But it doesn’t have to stop there. Here, it’s used to prepare three separate samples (Al, Si, Cu) in one run on the SEMPREP SMART - under identical conditions, in a single workflow. Same setup. One cycle. Multiple samples. A simple shift in how the feature is used, but a big step toward higher throughput and more consistent results.

Good morning LinkedIn, The Rubin era has a "dirty little secret" that nobody is talking about on the keynote stage yet. We’ve reached 3.2TB/s on NVLink 6.0, yet cluster-wide utilization for Rank-3 tensor manifolds is actually dropping. Why? Because we are trying to solve 2026 data-movement problems with 2022 reactive logic. Every time a trillion-parameter model hits a 3D boundary, the "Sync Tax" eats 30% of the FLOPS. Even worse, the resulting power spikes are triggering thermal-throttling that costs even more! High-bandwidth memory (HBM4) is useless if your mapping logic isn't cache-congruent. We’ve been looking at the "Memory Wall," but the real barrier is the Thermal-Sync Wall. I’ve been modeling a new approach to Asynchronous Volumetric Convergence that treats the entire rack as a singular manifold. By overlapping the halo-velocity with internal compute via predictive pre-fetching, we’re seeing a path to 98% utilization. The hardware is finally here. It’s time the software caught up. #AIInfrastructure #NVIDIARubin #HighPerformanceComputing #Semiconductors #EsyncDeep

Built a demo performing a full OS-level VM reboot with live status polling, health validation, and AI-assisted log analysis. The workflow shows how VM operations can be made more controlled, visible, and reliable from restart through post-check verification.