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What's New in Lakeflow Designer - October 2026 Roundup
Lakeflow Designer, a visual no-code data preparation tool in Databricks, added three new operators for cleaning, deduplication, and quality checks, addressing customer requests for more no-code capabilities. The Prepare operator standardizes data by changing case, converting types, replacing values, and applying formulas; Unique removes duplicates across selected columns with ordering control; Guardrails enforces quality rules with configurable failure handling. Existing operators gained multi-table joins with separate outputs for matched, left unmatched, and right unmatched rows, branching in Filter, and dynamic column selection in Select. Output options expanded to materialized views in Unity Catalog that refresh automatically, merge into existing tables, and direct file writes to CSV, Excel, or JSON with dynamic worksheets. Canvas improvements include a redesigned interface with better contrast and larger connection targets for large workflows, a searchable table of contents, visual version history, and live cursor presence.
Run fast, private agents on-device with LFM2.5-24B-A2B
LFM2.5-24B-A2B is a 24-billion-parameter reasoning model with only 2 billion active parameters per token, designed for fast, private on-device agents. It uses a Mixture-of-Experts architecture with 40 layers, 64 experts per block, and top-4 routing, combined with gated short convolutions and GQA. The model produces an explicit chain of thought before answering and supports tool calling and multi-step tasks. Pre-training scaled to 33 trillion tokens, followed by mid-training and a five-stage post-training pipeline including supervised fine-tuning, multi-domain distillation, and agentic reinforcement learning. On benchmarks, it leads or matches larger models on tool use and instruction following, achieving 48.09 on LiveClaw and 96.75 on IFEval, while decoding 184 tokens per second on an Apple M5 Max.
Why Your Sandbox Never Quite Matches Production (And Why That's Fine)
The gap between sandbox and production environments is structural and inevitable because they change on different schedules with different triggers. Infrastructure divergence rarely affects what integration tests catch, but behavioral contract drift—when simulated upstream responses no longer match real ones—causes tests to pass while production fails. The most effective teams connect simulation updates to upstream deployment events rather than to test failures, using observation-based tools like Keploy that capture real HTTP traffic to keep simulations current. Full production parity is not the goal; targeted accuracy for the specific behavioral contracts that integration tests validate is sufficient to catch the failures that matter most in distributed systems. The sandbox covers what it can, production reveals what it cannot, and the maintenance work is keeping the sandbox’s coverage accurate within its boundary, not expanding it to match production exactly.
Redesigning source control at Anthropic
At Anthropic, Claude’s ability to iterate on code at high speed has caused git-based source control to become the bottleneck in their development workflow. To handle the growth from 2,500 to over 30,000 daily code merges, the team built Scion, an in-house version control system, fully migrating their monorepo from git in five months. Scion diverges from git by assuming clients are always online, shifting computation to the server, and using horizontally scalable data structures without any fully materialized copy of the repository. The system stores changes as entries in a write-ahead log, supports sparse and virtualized checkouts, and enables cheap, lock-free commits that scale with change size rather than repository size. Anthropic shares Scion’s design principles to help the broader ecosystem prepare for AI-driven scaling challenges, noting that similar pressure points will emerge for other organizations as AI coding tools become more common.
Under the hood, off the hot path: How D1 scales to millions of databases
D1, Cloudflare’s serverless SQLite database, has seen 15x growth in live databases over the past year and scales by keeping query execution on the edge in Durable Objects, separate from the control plane’s metadata operations. The control plane uses an Edge API Worker with Hyperdrive to access Postgres clusters for transactional metadata, while Workers KV caches database-to-Durable-Object mappings for fast edge lookups. Database creation completes in about 1.6 seconds by generating a Durable Object ID, persisting metadata to Postgres, and storing the mapping in Workers KV, with the Durable Object lazily initialized on first query. Storage usage enforcement uses atomic counters in Workers KV that are checked locally on each query and updated asynchronously after execution, avoiding any central database round trip during the query. Precomputed aggregations for billing are stored in ClickHouse, and guiding principles such as eventual consistency and idempotency further reduce reliance on centralized components.
Making password login safer on Notion
Notion is requiring a second step of verification for password login to protect against credential reuse from other site breaches, and this change will roll out in stages over the next few months. When users set or change a password, they will be guided to set up two-step verification via an authenticator app or phone number and should save the backup codes provided. Users who already have two-step verification on their password need no action, and those without a password can continue using passkeys, email codes, or SSO. For password-only users without two-step verification, Notion will provide notice and time to set up a second step before turning off password-only login; if they choose not to, another method like an email code can still grant access. Additionally, where Notion can verify that another working method exists, it plans to turn off password login for those unused passwords.
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
Meta introduced GEM, the industry’s largest recommendation system foundation model, and its 2026 generation shifts from isolated per-domain optimizations on Facebook, Instagram, and Reels to a single central foundation model. The Kunlun architecture, co-designed with the latest GPU hardware, enables 2.5x model complexity and demonstrates LLM-like scaling laws with block-aligned sparse attention reducing sequence complexity. The LoopFM post-training technique transfers GEM’s internal representations to downstream models, doubling intelligence transfer at no additional compute cost or latency. GEM delivered 18.9% conversion improvement and 12% ad click-through rate improvement across all Meta ad surfaces and funnel stages, including 5.8% conversion uplift on Facebook, 4.1% on Instagram, and 1.6% on off-site conversion. Training efficiency doubled to 20-25% model FLOPS utilization while scaling total training FLOPs by 4x across thousands of GPUs using custom kernels and 5D parallelism.
Rewinding the exponential: How we keep hardware usage down
Cloudflare’s Data team manages a pipeline handling over two billion events per second from tens of thousands of servers, powering Logs, SQL API, and observability. To counter exponential traffic growth against linear hardware expansion, they implemented several optimizations over five years, deferring two and a half years of hardware expansion. In 2022, they replaced a monolithic schema with product-specific schemas, reducing backbone bandwidth by 35-40%. In 2024, they switched compression from LZ4 to zstd, achieving a 40% reduction in compressed traffic with negligible CPU impact. In 2026, they decoupled pipeline transit from database redundancy via a shared ingest pool and late replication, applied sampling downstream, and introduced aggregations, eliminating about 70 GB/s of compressed traffic and halving projected 2031 data volume.
Postgres at Open House NYC
At ClickHouse Open House NYC, the team announced WalShadow, an open-source engine that replicates Postgres into ClickHouse from the physical WAL, achieving sub-second latency and high throughput. They also launched ClickHouse Managed Postgres on Google Cloud in private preview, offering Postgres on local NVMe storage with native CDC into ClickHouse. The chdb Postgres extension was released, enabling fast imports from cloud storage using the embeddable ClickHouse engine. A live demo showed WalShadow keeping ClickHouse under 400 ms behind during a simulated trading platform with 50,000 trades per second, and detecting a fraud burst in under 500 ms compared to over 15 seconds with ClickPipes. The integrated stack aims to provide world-class Postgres for transactions and ClickHouse for analytics, with WalShadow as a key step toward unifying OLTP and OLAP.
Behind the messenger: making messaging human - The Intercom Blog
Intercom redesigned its messenger to make business communication personal, moving away from the anonymity of the early internet. The post argues that sharing real names, faces, and job titles builds trust, as user testing showed customers repeating the names of the reps they messaged. System-generated text like active status, location, and local time that cannot be changed adds authenticity and helps users understand the asynchronous nature of the system. The post recounts an incident where a customer doubted a rep’s real name, highlighting the need for transparency, and notes that job titles prevent white lies like claiming to pass feedback to a product team when the rep is on that team. The post concludes that these small details together build trust, leading to customer loyalty and forgiveness when things go wrong, and that users initially don’t expect this information but quickly come to expect it everywhere.
Basecamp's Jason Fried on product strategy - The Intercom Blog
Jason Fried, co-founder of Basecamp, discusses the company’s approach to product strategy, including when to undertake a major product overhaul. Basecamp releases ground-up versions every few years when incremental updates reach a local maximum, and it supports older versions indefinitely to avoid forcing customers onto its schedule. Fried argues that a chat-first communication mindset can be toxic and that Basecamp’s tools are designed for asynchronous, organized communication rather than real-time chat. Despite generating tens of millions in revenue and profits, Basecamp deliberately keeps its team small to avoid the pressure of a large organization and to focus on creating great work. Fried advocates for careful growth over rapid scaling, a philosophy he plans to explore in a future book titled “Regrow.”
Facebook's Julie Zhuo on product design - The Intercom Blog
Julie Zhuo, Facebook’s VP of Product Design, recounts her evolution from engineering intern to leading design for the core Facebook app, including News Feed and the Like button. She defines good design as an experience that is enjoyable, useful, easy, and accessible, noting that adding functionality without increasing complexity is a persistent challenge. There is no fixed ratio of designers to engineers; the ideal mix depends on the stage of achieving product-market fit. Effective design principles should be controversial, as they articulate why a company made a particular choice over alternatives. When competing products offer similar utility and ease, the ultimate differentiator is the emotional response the product evokes in the user.
Bob Moesta on Jobs-to-be-Done - The Intercom Blog
Bob Moesta, a principal architect of the Jobs-to-be-Done framework, explains that JTBD interviews focus on uncovering the actions and progress a buyer is trying to make, not opinions or product features. He describes the key forces driving customer switching—push, pull, anxiety, and habit—and notes that innovation opportunities lie at the edge where people want to be better, not with habitual users. Moesta introduces the concepts of big hire (purchase) and little hire (actual use), emphasizing that value is delivered in the latter and that both must be designed for. He addresses zombie revenue, which can arise when customers move on after the job is done, don’t understand the product, or when the product serves a different job than intended. For startups new to JTBD, Moesta advises removing their product from the interview equation and starting with competitor customers to understand the context and outcome independently of the product.
Clearleft's Andy Budd on designing for users - The Intercom Blog
Andy Budd, co-founder of Clearleft, discusses why design is a critical differentiator for startups, noting that companies like Slack and Dropbox succeeded through superior user experience rather than being first to market. He advises hiring the best designers you can afford early to solve existential business problems, then bringing in a more junior team to evolve and maintain the product. Consumer expectations for digital design have risen greatly, but startup budgets haven’t kept pace because few designers hold budget-making positions, creating a misalignment. Agile development has a place, but companies must balance it with longer-term systems that make design, technology, and products scalable. For voice-activated interfaces, designers will still need to define decision trees and logic, shifting from graphic design to conversational and personality design, as seen with tools like x.ai’s Amy.
Tomasz Tunguz on Venture Capital - The Intercom Blog
Tomasz Tunguz, a partner at Redpoint Ventures, discusses the key metrics he looks for in potential investments, including sustainable competitive differentiators and revenue benchmarks. He identifies three types of sustainable competitive advantage: a founder’s market discovery, difficult-to-replicate technology, or a new distribution mechanism. For Series A investments, he typically partners with companies at $50,000-100,000 MRR, but also invests in pre-revenue ideas, noting that 27% of Series A rounds had no revenue. He looks for short sale cycles under 45 days and negative net churn as signs of product-market fit, and advises founders to always have a plan to reach profitability on current cash. Investors view businesses as collections of risk, and founders should address how they will mitigate major risks in their pitch.
Stop focusing on the next big thing - The Intercom Blog
The post criticizes the tendency of product people to fetishize emerging trends like AI, VR, and AR without stepping back and asking why, latching onto hype rather than focus. It argues that this ‘Next Big Thing-ism’ is rooted in short-term jostling and FOMO, not focus, leading to products that seek quick exits, as seen at CES or on Product Hunt. The author advocates for a humbler, more considered approach that treats product building as an opportunity to gradually influence the future, perhaps by kicking stones in its path, rather than leaping forward for a secret advantage. This involves designing the rudder, not the boat, and recognizing that there is no inevitable endpoint in design, only hypotheses about what could come next. As Brian Eno said, the aim is to make things that belong to the future we would like to live in, not to chase hype for competitive advantage.
Unflattening design - The Intercom Blog
Design problems are inherently multi-dimensional and require multiple perspectives to understand fully, as a single viewpoint flattens the challenge. The post uses the metaphor of ‘unflattening’ from Nick Sousanis’ graphic novel to argue that designers must break out of habitual, flat viewpoints to see a richer picture. It recommends alternating between open exploration and closed execution, and using techniques like changing context, applying constraints such as mobile-first design which led Uber to strip back to one-button ordering, and zooming in and out on problems to see both details and the big picture. Borrowing mental models from others and externalizing ideas through artifacts like Post-it notes, storyboards, or prototypes helps clear the mind to make room for new insights, as Sousanis explains that recognizing our solitary standpoint is limited. Ultimately, design is a modeling activity that benefits from constantly manipulating and enriching one’s perspective to create better decisions.
Intercom's Paul Adams on product - The Intercom Blog
Paul Adams, VP of Product at Intercom, shares product management principles from his experience at Google and Facebook, where he worked on Gmail, YouTube, and Facebook Ads. He advocates for small, resource-constrained teams with a 1:1:5 ratio of product manager, designer, and four to five engineers, arguing that limited resources force focus on what matters. Adams introduces a ‘666 roadmap’ framework: a six-year vision, six-month strategy, and six-week execution cycles to balance long-term goals with daily work. He emphasizes aiming for 10x improvements rather than incremental 10% gains, and predicts a shift from app icons to apps as services, citing WeChat as an example. For startups, he advises ignoring annual tech predictions and instead focusing on long-term vision to avoid distraction.
MarketingProfs' Ann Handley on content marketing - The Intercom Blog
Handley, Chief Content Officer at MarketingProfs, was an early practitioner of content marketing to engage customers. She emphasizes empathy, thinking from the customer’s point of view rather than the company’s. For B2B marketers, she advises telling a bigger story beyond the product, citing Slack’s podcast about work culture and innovation. She recommends measuring content quality by whether customers would thank you for it, a simple test that clarifies value. Handley also highlights writing with personality and the label-off test, where stripping branding reveals if content sounds distinctive, and her book “Everybody Writes” inspires companies to use words better as ambassadors for their brand. The best marketing doesn’t feel like marketing and should be content people want or enjoy.
Design is a conversation - The Intercom Blog
Conversational interfaces represent the next step in removing abstraction between humans and machines, moving from punch cards to command lines to multitouch and now to dialogue. Bots are software agents that run inside messaging apps, performing tasks like ordering a taxi through simple text commands, and are straightforward to write as simple scripts. This shift is compared to the Web 2.0 API revolution, where bots become a platform for building new user experiences, similar to how APIs transformed websites into services. Designing conversational UIs draws inspiration from interactive fiction and text adventure games, which used branching conversation trees, but the post notes these are only starting points. The post argues that conversation as a user interface creates new opportunities and challenges for designers, with messaging apps becoming the layer through which daily life is conducted, as evidenced by six of the top ten apps being primarily for messaging.
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