u/Equal_Winter3150

With today's Meta (8000) cuts and the wave of tech layoffs this year (110,000+ across 137 companies so far in 2026), a lot of people are about to enter a job market that looks different from what they're used to.

If you've been in big tech, your interview experience is probably coding rounds, system design, and behavioral questions. That's the loop you know how to prepare for.

But a huge number of companies outside big tech use cognitive assessments as part of their hiring process. Companies like Spring Health, Stryker, General Mills, and hundreds of others use the PI Cognitive Assessment, a timed 50-question test that measures how quickly you process information under pressure. You usually get the link after the recruiter screen and before deeper interviews.

The reason this catches people off guard is that it's not about what you know. It's a speed test. 50 questions, 12 minutes, covering verbal reasoning, numerical reasoning, and pattern recognition. Most people don't finish, and that's by design. Your score determines whether you move forward.

A few things I wish someone had told me before I took one:

• There is no penalty for guessing. If you're running out of time, fill in everything.
• The questions rotate randomly, so you can't memorize answers from a study guide. What you can do is get comfortable with the format and pacing.
• 14 seconds per question is the average. If you spend more than 20 seconds on anything, skip it.
• Verbal questions (antonyms, analogies, sentence completion) are usually the fastest to answer. Numerical word problems take the longest. Know your strengths and play to them.
• Take it in a quiet room, on a computer, with nothing else open. The 12 minutes go faster than you think.

If you've never encountered one of these before, taking even one timed practice test beforehand makes a real difference. You don't want the first time you see the format to be the real thing.

Happy to answer questions if anyone is prepping for one.

I've been reading up on alternative paradigms beyond standard gate-based quantum computing — specifically topological quantum computing and neuromorphic quantum architectures. The argument is that as quantum hardware matures, these approaches could offer real structural advantages in error correction and scalability rather than just being theoretical curiosities.

Topological qubits encoding information in global properties rather than local states is compelling from an error-resilience standpoint, and the idea of merging quantum mechanics with brain-inspired adaptive architectures feels like it could open up entirely different classes of problems.

Curious what this community thinks. Are these paradigms getting overhyped relative to where the actual hardware is? Or are we underestimating how quickly they could become practical?

This article covers it well for anyone interested: https://medium.com/@monendra.grover/beyond-qubits-the-rise-of-topological-and-neuromorphic-quantum-machines-5736fe79da4a

LangChain just wrapped of Interrupt 2026 and announced a few things worth knowing about:

SmithDB — A purpose-built distributed database for agent observability. The problem they're solving: agent traces are getting too large and complex for general-purpose databases. SmithDB is built with Rust, Apache DataFusion, and Vortex, designed specifically for multimodal content and long-span tracing. They're reporting P50 latency of 92ms for loading trace trees and 400ms for full-text search, with up to 12x speedup over previous LangSmith performance. Architecture is object storage + small Postgres metadata store + stateless services, so it scales elastically and can be self-hosted.

Context Hub — A centralized system for managing agent context (AGENTS.md files, skills, policies, memory) in LangSmith. The interesting part is they're working with MongoDB, Pinecone, Elastic, and Redis on an open standard for agent memory — covering episodic, semantic, and procedural memory with versioning and portability across frameworks.

Deep Agents v0.6 — New release includes ContextHub Backend integration, an installable code interpreter that gives agents a programmable workspace inside the agent loop (distinct from sandboxes — this is for composing tools and managing state within the reasoning process), and you can scope specific file paths to different backends.

The conference also has production case studies from Toyota, Coinbase, Lyft, LinkedIn, Bridgewater Associates, and others on deploying agents at enterprise scale. Andrew Ng keynoted alongside Harrison Chase.

LangChain just wrapped Day 1 of Interrupt 2026 and announced a few things worth knowing about:

SmithDB — A purpose-built distributed database for agent observability. The problem they're solving: agent traces are getting too large and complex for general-purpose databases. SmithDB is built with Rust, Apache DataFusion, and Vortex, designed specifically for multimodal content and long-span tracing. They're reporting P50 latency of 92ms for loading trace trees and 400ms for full-text search, with up to 12x speedup over previous LangSmith performance. Architecture is object storage + small Postgres metadata store + stateless services, so it scales elastically and can be self-hosted.

Context Hub — A centralized system for managing agent context (AGENTS.md files, skills, policies, memory) in LangSmith. The interesting part is they're working with MongoDB, Pinecone, Elastic, and Redis on an open standard for agent memory — covering episodic, semantic, and procedural memory with versioning and portability across frameworks.

Deep Agents v0.6 — New release includes ContextHub Backend integration, an installable code interpreter that gives agents a programmable workspace inside the agent loop (distinct from sandboxes — this is for composing tools and managing state within the reasoning process), and you can scope specific file paths to different backends.

The conference also has production case studies from Toyota, Coinbase, Lyft, LinkedIn, Bridgewater Associates, and others on deploying agents at enterprise scale. Andrew Ng keynoted alongside Harrison Chase.

DeepMind dropped their "one year of AlphaEvolve" impact post today. Most of it is the corporate highlight reel — TPUs, Klarna, FM Logistic, etc. — but the quantum item caught my eye, and I think the underlying paper deserves more attention than the blog post gives it.

The blog says AlphaEvolve found "quantum circuits with 10x lower error than previous conventionally optimized baselines" for molecular simulation on Willow. That framing is doing a lot of work. The actual paper (Cao et al. 2510.19550) is more specific and more interesting.

The setup: they're measuring OTOCs experimentally on organic molecules (toluene and 3',5'-dimethylbiphenyl) via NMR, with the molecules in a nematic liquid crystal. The OTOCs encode structural information that's classically expensive to interpret. So they use Willow to simulate the OTOC dynamics, and feed that back to estimate molecular geometry — mean ortho-meta H-H distance for toluene, mean dihedral angle for the biphenyl. The quantum simulation lets them invert what would otherwise be an exponentially-costly classical reconstruction.

AlphaEvolve's specific contribution is evolving a first-order Trotter formula generator and producing a novel product formula algorithm tuned to this Hamiltonian and gate set. Combined with Pauli-pathing-based zero-noise extrapolation, they hit RMSE 0.05 over all circuits used. The "10x error reduction" framing in the blog refers to circuit error, not the structural learning task itself.

A few things I'd want to engage with:

* The Trotter optimization is the kind of thing that's traditionally hand-tuned by the algorithms team for each new problem. Having a system evolve product formulas automatically is genuinely useful and could compound across simulation problems on Willow-class hardware. But I'd want to know how well the optimized formula transfers — is this a one-off for these specific molecules and this specific gate set, or do the evolved formulas generalize?
* The OTOC structural learning protocol is interesting in its own right, AlphaEvolve aside. Using a quantum computer to interpret NMR data this way is a real proposed application for near-term hardware, not the usual "factoring is coming someday" handwave.
* The framing question I keep coming back to: is this "AI designs quantum circuits → quantum advantage closer" (DeepMind's framing) or "classical AI tooling is getting good enough to extract more from current NISQ hardware" (probably the more honest framing)? Both stories are interesting; they have different implications.

Curious if anyone here has read the paper carefully. *The OTOC-as-spectroscopy-tool angle is probably underexplored relative to the AlphaEvolve hook.*

Paper: [https://arxiv.org/abs/2510.19550\](https://arxiv.org/abs/2510.19550)

DeepMind impact post (for context): [https://deepmind.google/blog/alphaevolve-impact/\](https://deepmind.google/blog/alphaevolve-impact/)

I come from a software development background, and it feels like AI is rapidly automating many parts of software engineering. As coding and technical tasks become more automated, I wonder if companies will start placing more weight on cognitive skills and soft skills like communication, empathy, adaptability, collaboration, and cultural fit.

Curious to hear from others here:

How are you preparing for this shift? Are you changing how you practice for interviews? What tools, platforms, or resources are you using to prepare?

Our 2023 paper just got its journal version published in APL Machine Learning, so this feels like a reasonable moment to share it here and reflect on what held up vs. what I'd do differently. Open access link: https://pubs.aip.org/aip/aml/article/3/3/036106/3355997

The question: do hybrid classical-quantum models offer something qualitatively different from pure classical, or are they just an expensive path to accuracy parity? Most QML papers benchmark on clean-data accuracy, hit roughly classical performance, and call it a day. We wanted a property where the comparison would be more meaningful, so we tested adversarial robustness on histopathological cancer detection.

Setup: classical feature extractors (ResNet18, VGG-16, Inception-v3, AlexNet) integrated with multiple VQCs of varying expressibility. Compared against the same backbones without quantum components. Adversarial inputs generated via FGSM, PGD, and similar standard attacks. PennyLane simulators throughout.

Finding that's held up: the hybrid models degraded less under attack than classical baselines. Consistently, across attacks and extractors. Clean-data accuracy was comparable; the robustness delta was where the qualitative difference showed up.

What I'd change two years on, to be honest about it:

1. The mechanism story is weaker than I'd like. We hypothesized that the robustness gain comes from VQC structure constraining the loss landscape, but distinguishing that cleanly from regularization effects requires controls we didn't run. If I were redoing this, I'd ablate the quantum component while preserving parameter count.
2. Simulator-only is a real limitation. NISQ devices have improved enough since 2023 that some version of this experiment could plausibly run end-to-end on real hardware. That's the natural follow-up.
3. The attack suite was standard for the time but limited. AutoAttack, more recent transfer attacks, and adaptive attacks would be appropriate to add now.
4. The choice of histopathology felt unusual at the time of submission. In retrospect, it was the right call — pixel-level perturbations to medical images aren't an academic abstraction, and adversarial robustness in safety-critical imaging is exactly where this kind of comparison earns its keep.

Why I'm sharing it now rather than at preprint time: many 2023-era QML accuracy claims didn't survive contact with stronger classical baselines. The robustness claims have aged better, partly because they're about behavior under perturbation rather than absolute performance. I think that distinction matters for what kinds of "quantum advantage" claims are worth pursuing on near-term hardware, and I wanted to put the paper in front of this community now that we have some perspective on it.

Curious what people here think — especially anyone working on adversarial ML or NISQ-era QML applications. Pushbacks welcome.