Software Engineer

Perplexity and American Express launch ai-powered business Skills for Card Members. American Express and Perplexity have launched a suite of pre-built AI workflows, known as Skills, specifically for U.S. Business Card Members. By automating complex tasks like cash flow forecasting and tax preparation through direct financial data integration, the partnership signals a significant shift toward embedding generative AI directly into the operational fabric of small and medium-sized enterprises. What was announced. The partnership introduces "Skills for Perplexity Computer," a collection of ready-to-use AI workflows designed for U.S. American Express Business Card Members who hold an active Perplexity Enterprise subscription. These Skills function as reusable sets of instructions that guide the AI through specific operational tasks, removing the need for business owners to draft complex prompts from scratch. Instead, users provide specific parameters through a form - such as a date range for a forecast or a target audience for a campaign - and the system executes the workflow. The collection is broad, covering finance, marketing, operations, and human resources. Key financial Skills include: * Cash flow forecasting: Uses bank balances and transaction history via Plaid to build week-by-week projections and identify potential shortfalls. * Tax-season prep: Organizes transactions and flags unusual expenses for accountant review. * Reconciliation: Matches card transactions to invoices and receipts, flagging missing records or duplicates. * Marketing campaign generation: Drafts copy for emails and social media using brand voices and historical data from platforms like HubSpot and Mailchimp. * Operations monitoring: Reviews metrics from Google Sheets or Snowflake to identify bottlenecks. To access these tools, eligible card members must open "Personal CFO" from the Perplexity connectors page and link their American Express Business Card through Plaid. Accessing these workflows requires Computer Credits, with the cost varying based on the complexity of the task. Business owners are encouraged to review all AI-generated results before making decisions or putting drafts to use. "Skills are reusable sets of instructions that guide Perplexity Computer through specific tasks, including the information it needs and the output it should produce. With this collection, business owners select a task and provide details through a form, such as the focus of a campaign or the period a forecast should cover. They don't have to write a detailed query from scratch." Perplexity The companies involved. American Express is a cornerstone of the global financial services industry, particularly dominant in the corporate and small business credit sectors. With 109 previous reports on the company by FF News, Amex has consistently focused on expanding its digital ecosystem for business owners. The company provides a wide array of credit cards, charge cards, and travel-related services, but has increasingly pivoted toward integrated software solutions that help businesses manage their back-office operations and working capital. Perplexity has quickly established itself as a major player in the generative AI space. FF News has covered the company 13 times, tracking its evolution from an AI-powered search engine to a provider of sophisticated enterprise tools. Its "Perplexity Computer" represents a shift toward agentic AI - systems that can interact with external data sources and perform multi-step tasks rather than just generating text. By targeting the enterprise market through subscription models, Perplexity is positioning its technology as a productivity layer that sits on top of existing business data and software stacks. What FF News has reported before. FF News has closely followed American Express's aggressive push into the intersection of finance and artificial intelligence. In October 2026, Ascento Capital reported on how American Express Reinvents Business Spending with New Amex Corporate Cashback Cards and AI Tools, highlighting the company's strategy to blend traditional credit products with advanced automation. This followed a September 2026 report titled Global CFOs Shift Focus to Cash Flow Management and AI Automation Over External Risks, which detailed a growing trend among financial leaders to prioritize internal efficiency and automated cash management over external macroeconomic concerns. These previous stories underscore a consistent trajectory for Amex, as it seeks to provide business owners with tools that mitigate the complexity of financial forecasting and operational oversight through technological intervention. What this means. This move signals the transition of generative AI from a novelty search tool to a functional "Personal CFO" layer. By integrating with Plaid and major marketing platforms, Perplexity is moving into territory traditionally held by specialized SaaS providers. The industry should note the pressure this puts on traditional accounting and ERP software; if a business owner can generate a reliable cash flow forecast or reconcile expenses via a simple AI Skill, the value proposition of standalone financial management tools may diminish. However, the reliance on "Computer Credits" and the necessity of linking sensitive financial data via Plaid raises significant questions about the cost-to-value ratio and the long-term security implications of feeding real-time transaction data into large language models.

AI Daily | Kolibri Open-Source MoE Model, Perplexity Visualize Feature, and Meta RankEvolve Multi-Agent Framework. AI Daily | Kolibri Open-Source MoE Model, Perplexity Visualize Feature, and Meta RankEvolve Multi-Agent Framework Model Releases & Updates Kolibri - Aleph Alpha - Aleph Alpha Bottom line:Aleph Alpha has released Kolibri, a 78.1B parameter open-weight... PUBLISHED 2026.10.04 READING TIME 4 MIN UPDATED 2026.10.04 COMMUNEIFY INSIGHTS Distilling complex AI dynamics into thoughtful, focused intelligence. COMMUNEIFY / 26 Model Releases & Updates. Kolibri - Aleph Alpha - Aleph Alpha. * Bottom line:Aleph Alpha has released Kolibri, a 78.1B parameter open-weight German-English bilingual MoE model featuring a 1M token context window under the Apache 2.0 license. * Architecture:Employs a Mixture-of-Experts architecture with 78.1B total parameters and 3.46B active parameters per token. * Context & Openness:Supports up to 1 million tokens of context and is fully open-weighted under the Apache 2.0 license. * Source:Tech Report * Source:Hugging Face Sopro V2 turbo 2610 - samuel vitorino - samuel vitorino / localllama. * Bottom line:An interim update to the lightweight Sopro text-to-speech model focusing on cleaner voice cloning and reduced roughness while maintaining a 120M parameter size. * Performance:Achieves ~300ms to first audio on laptop CPUs with a 120M parameter architecture. * Languages:Supports English, European Portuguese, French, and German under an Apache 2.0 license. * Source:GitHub Repository * Source:Hugging Face Weights Product Releases & Updates. Visualize Feature in Perplexity Computer - Perplexity - Perplexity. * Bottom line:Perplexity introduced a new Visualize capability in Computer, generating inline interactive components and animations directly within user chat responses. * Functionality:Users can prompt queries starting with 'Visualize' to receive dynamic educational widgets and animations. * Recommendation:Recommended for use under Standard or High effort modes for optimal visual output. * Source: 在 X 上查看 @AravSrinivas 的貼文 Extract v2.5 - llamaindex - llamaindex. * Bottom line:LlamaIndex launched Extract v2.5, a series of frontier agents tuned for precise multi-page document extraction and complex tabular parsing. * Performance:Outperforms Opus 5.5 and GPT-6 Sol on complex long-list and multi-page table extractions while being 30% to 4x cheaper. * Accuracy Gains:Achieved a jump from 86.1% to 95.5% on complex extraction tasks using the agentic tier. * Source:Blog Announcement Industry news. AWS ends government NDA usage for data centers - amazon web services - amazon web services. * Bottom line:AWS CEO Matt Garman announced that the company has stopped using non-disclosure agreements when partnering with government agencies on data center construction. * Policy Shift:Eliminated NDAs for government-linked data center projects to increase transparency amid public scrutiny. * Resource Defense:Garman noted that direct data center water usage accounts for only 0.5% of US industrial consumption and highlighted $1 billion in community investments. * Source:TechCrunch Coverage Cloudflare launches next-gen Git platform challenge - Cloudflare - Cloudflare. * Bottom line:Cloudflare announced an open beta for Artifacts and launched a developer challenge to build multi-agent Git platforms using Workers and Artifacts. * Competition Details:Invites developers to build agent-era Git platforms requiring multi-agent concurrency, running until October 14, 2026. * Prizes:Offers $25,000 in Cloudflare credits for the first-place winner. * Source:Cloudflare Blog Research papers. RankEvolve: Multi-Agent auto-research framework - Meta - Meta. * Bottom line:Meta introduced RankEvolve, a multi-agent framework where coding models cross-review and refine research code modifications under compilation protocols. * Methodology:Leverages models like Claude Code and Codex as independent nodes to mutually review and patch code changes. * Results:Boosted task execution accuracy from 45.8% to 62.5% and improved recommendation model benchmarks. * Source: 在 X 上查看 @dair_ai 的貼文 The sharpening tax in RL post-training - Meta superintelligence labs - Meta superintelligence labs. * Bottom line:Researchers discovered that reinforcement learning post-training improves pass@1 accuracy but imposes a 'Sharpening Tax' that limits test-time scaling performance. * Finding:Base models with lightweight inference frameworks frequently outperform RL-tuned models when given sufficient sampling budgets. * Proposed Solution:Introduced PTGS to estimate prompt difficulty and set sampling temperatures to mitigate the scaling tax. * Source: 在 X 上查看 @omarsar0 的貼文 ThinkingBox agent sandbox & benchmark - microsoft & Hugging Face - microsoft / Hugging Face. * Bottom line:Microsoft and Hugging Face released ThinkingBox, an agent sandbox and benchmark designed to evaluate stateful business workflows using database end-states. * Scope:Covers 507 stateful business workflows with 20 repeated evaluations per task. * Execution:Uses executable evaluation based on final database states and side effects, accessible via OpenEnv. * Source:Hugging Face Blog Other highlights. Advocating default hard budget caps for metered AI services - Simon Willison - Simon Willison. * Bottom line:Developer Simon Willison published an essay calling for metered AI services and APIs to provide mandatory default hard budget caps to prevent runaway agent bills. * Core Argument:Usage-based services should immediately cut off and return errors upon hitting limits rather than relying solely on warning emails. * Ecosystem Impact:Autonomous coding agents lower the barrier to deploying paid code, escalating the risk of accidental large invoices. * Source:Blog Post END OF NOTE Experience Scribis: ultimate AI audio & video workflow. Scribis is an all-in-one AI audio tool designed for creators, researchers, and professionals. It combines local privacy computation with cloud API integration, providing millisecond-level speech-to-text, speech synthesis, and dynamic timeline editing.

Perplexity details Photon, its search retrieval engine. Perplexity says Photon handles retrieval and ranking in its production search stack. Its reported 65 ms p99 covers Photon's stages; Fast Search is a separate API mode. September 30, 2026 Key points * 01 Perplexity describes Photon as the internal engine that retrieves and ranks candidate web pages for its production search. * 02 Perplexity reports a production p99 of about 65 ms for Photon's stages, compared with about 800 ms for the previous retrieval-and-ranking system. * 03 Fast Search is a separate Search API mode built on Photon, with reported single-call latency of 160 ms p50 and 230 ms p95. * 04 Across six benchmarks and 3,554 selected tasks, Perplexity reports a 64.3% score for Fast Search and 64.0% for its default preset. * 05 Fast Search costs $1 per 1,000 successful API requests, according to Perplexity's documentation. Perplexity says Photon handles search retrieval and ranking at a 65 ms p99. See what that figure covers and how Fast Search's API metrics differ. On September 24, 2026, Perplexity announced the Fast Search option for its Search API and described Photon, the internal retrieval-and-ranking engine it says powers its production search. Perplexity reports a p99 latency of about 65 milliseconds for Photon's stages, down from about 800 milliseconds in its previous system. Fast Search is a separate API mode built on Photon, with its own latency and benchmark figures. What Photon does in Perplexity search. Photon retrieves candidate web pages and ranks them for Perplexity's search stack; it is not a standalone consumer search engine. Perplexity says the system replaced an adapted open-source engine as its index and workloads grew, and places Photon within its broader move to in-house Rust-based search infrastructure. How a query moves through Photon. A request passes from a load balancer to Photon's broker, which selects a group of shards. The shards retrieve and rank candidate pages; the broker then merges those candidates and fetches key fields for selected documents before returning results to the higher-level search system. Photon uses an inverted index, which maps terms to the documents that contain them. It stores compact per-document ranking records called "docblobs" and uses different representations for short and long posting lists. A WAND-like traversal uses score bounds to limit which candidates need further ranking. How Perplexity builds and serves its index. Perplexity separates index construction from live query serving. Pillar prepares source data in YTsaurus tables, and indexers build shard structures from that data. Versioned index files are stored for deployment; a controller rolls new index versions out across serving groups. That separation also shapes the reported operating figures: Perplexity says Photon uses about 20% fewer equivalent serving machines than the previous system and stores about 2.5 times as much data per document. The company says it can build its full web index in a single-digit number of hours. What Photon's reported p99 latency measures. Perplexity's production p99 comparison covers the retrieval-and-ranking system: about 800 ms for the previous engine and about 65 ms for Photon. The 65 ms figure covers Photon's internal stages and excludes later stages in the higher-level search stack. | System | Reported production p99 | Measurement scope | | Previous retrieval-and-ranking system | About 800 ms | Retrieval and ranking in Perplexity's production context | | Photon | About 65 ms | Photon's internal stages; later search-stack stages are excluded | Fast Search benchmarks and API access. Fast Search combines Photon with a ranking configuration tuned for agent workflows. Perplexity reports 160 ms p50 and 230 ms p95 for a single Fast Search API call. Those figures describe API-call latency, not Photon's production p99. The p50 is the median; p95 is the 95th-percentile latency. Across six benchmarks and 3,554 selected tasks, Perplexity reports a 64.3% task score for Fast Search and 64.0% for its default preset. The company's estimated model-plus-search costs for that benchmark set were $59.73 and $187.60, respectively. These are estimated costs across the tasks, not per-request API prices. | Perplexity-reported measure | Fast Search | Default preset | Context | | Task score | 64.3% | 64.0% | Six benchmarks; 3,554 selected tasks | | Estimated model-plus-search cost | $59.73 | $187.60 | Same benchmark task set | | Long-tail DCG relevance | 2.21 | 2.45 | Separate internal tests | | Answer availability | 0.567 | 0.596 | Separate internal tests | The long-tail results put the small benchmark-score difference in context: Perplexity reports lower DCG relevance and answer availability for Fast Search than for its default preset in separate internal tests. To call Fast Search, the Search API uses search_type: "fast" on POST /search; the max_results setting can range from 1 to 20. Perplexity's documented tariff is $1 per 1,000 successful requests. Other Languages Read this article in another available language.