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Perplexity launched AI colleagues Teammates, with an internal number reaching 20 times the number of employees

AI search company Perplexity officially launched Teammates, allowing AI Agents to be responsible for specific tasks long-term like employees. CEO Aravind Srinivas revealed that approximately 10,000 AI Teammates have been created internally in the last 30 days, which is 20 times the total number of human employees.Teammates are built on Perplexity Computer. After users assign roles, goals, and permissions to the AI, it can continuously track work progress, proactively identify issues, and call upon tools to address them. Multiple Teammates can also divide tasks among themselves and contact human colleagues via Slack. For example, a sales Agent can discover that a customer has not placed an order for a long time, automatically investigate the reasons, and prepare a follow-up plan; a development Agent can hand off the task of writing a fix code to another Agent after discovering a website bug. Each Agent has an independent working environment, retaining project memory and task history, allowing users to check progress, adjust direction, or take over operations at any time.Business Insider exposed Perplexity's internal Teammate tool in July this year, which was primarily used for software engineering tasks such as bug tracking and system monitoring. The product has now expanded to marketing, sales, customer service, and operations, adding long-term task management and cross-Agent collaboration. Teammates are currently available in research preview form to Max subscription users at $200 per month, initially supporting the web version, with native mobile and desktop versions to be launched later.

first_img Goldman Sachs restricts traders and bankers from using certain AI programming tools

According to Bloomberg, Goldman Sachs has restricted most employees from using advanced AI tools aimed at programming after evaluating production efficiency and risk management, leading to dissatisfaction among some employees. Internal guidelines were updated in October, and bankers had previously been able to use Anthropic's Claude Code and Microsoft's GitHub for several months, but the scope of these tools has now been narrowed to engineers.Sources say that some traders had used these tools to accelerate monitoring of client trading flows, modeling market changes and positions, as well as creating text and graphic content, and this restriction has left them feeling dissatisfied. Goldman Sachs' Chief Information Officer Marco Argenti stated in July that non-technical personnel such as traders and bankers had developed solutions using AI tools, which were then handed over to the company's approximately 12,000 engineers for improvement.Goldman Sachs spokesperson Nick Carcaterra stated that the company provides employees with certain AI tools to determine applicable scenarios in different businesses; after evaluating factors such as production efficiency and risk management during the trial period, the tools will be given to the most suitable personnel for long-term use. Additionally, sources indicated that the latest directive does not apply to strats who are simultaneously engaged in trading, quantitative research, and software engineering. Earlier this year, Goldman Sachs also restricted employees in Hong Kong from using Anthropic's Claude.

first_img Proton CTO: Programming is dead, engineers should help models write code

The privacy email and VPN service provider Proton publicly shared an internal memo titled "AI @ Proton, Engineering Manifesto," written by Chief Technology Officer Bart Butler about a month ago. The memo states that software engineering has undergone irreversible changes this year, as large language models and their tools can now write code. A senior Proton engineer indicated that no one in their business unit is writing code by hand anymore. Butler believes that the era of manually writing excellent code is over; time-consuming tasks such as documentation, testing, and boilerplate code can be handled by models, which can also participate in architecture and system design discussions.Butler pointed out that although engineers are widely using large language models, Proton's productivity has not significantly improved for three reasons. First, the engineering culture needs to shift; the work of engineers is no longer to write code but to help models write better code faster. Each engineer should become a team leader for models and agents, promoting their autonomous and continuous work, and reducing repetitive failures through repository-level context, shared skills, and prompts. Second, the company is still in the first phase of using models to accelerate existing processes and needs to enter the second phase, which involves eliminating bottlenecks across continuous integration, design, approval, specifications, and code review, with the expectation that most manual code reviews will come to an end. Third, there is a collective action problem between individual use of models and the overall transformation of company processes, as the organization has not invested enough resources.The memo emphasizes that this is not the end of expertise or good engineering practices; maintainability, documentation, and testing remain critical, but best practices will adjust with changes in cost and capability. Butler wrote, "Programming is dead," but engineering and professional skills have not vanished. The new responsibility of engineers is to continuously optimize and maximize automation as builders, and Proton will organize and allocate resources to complete this transformation. Proton stated that the memo is not an in-depth discussion of engineering documents related to security, reliability, or infrastructure. It also mentioned that technology companies have laid off over one million people in recent years, and Microsoft proposed 60,000 buyout offers last week.

first_img Yield Basis launched the permissionless propAMM, prioritizing the deployment of the WETH pool

The automated market-making protocol Yield Basis, founded by Curve founder Michael Egorov, announced the launch of a permissionless propAMM, aiming to upgrade its liquidity mechanism on Curve. This design combines market perception pricing with dynamic fees, aiming to increase trading volume, reduce arbitrage losses, and improve liquidity provider returns. According to its simulations over the past 18 months, Yield Basis's WETH position is projected to grow at an annualized rate of 8.5% in ETH terms under the new design, compared to 3.6% under existing parameters.Officials stated that WETH liquidity providers do not need to take additional actions, as the new design will directly affect their respective liquidity pools. The small transaction fee for pulling the pool price back to the market price is 0.50%, and anyone can submit prices or deposit funds, with the relevant code being open source. The WETH pool will be deployed first, and Bitcoin-related liquidity pools will follow after the successful deployment of the WETH pool.According to the announcement, the liquidity pool will charge based on the profits earned by arbitrageurs from trades, retaining about two-thirds of the profits above the minimum fee. The new design features lower single transaction fees, with simulated trading volumes approaching three times the current levels, requiring donations of about one-quarter of the current amount. The Yield Basis DAO owns the strategy, and the Curve Emergency DAO can shut it down in emergencies, while the Curve liquidity pool contract itself remains unchanged. Curve DAO vote 1508 will connect the strategy to the WETH pool and set the reserved profit ratio, while Yield Basis vote 60 will set the LevAMM fee at 1.5% (currently 1.3%), with the donation rate being about 1.2% of the pool value per year (currently about 5%). The above simulations only include arbitrage exchanges and do not account for regular trading and liquidation.

first_img Storage substrate manufacturer TLB launches SOCAMM3 substrate development

Korean storage substrate manufacturer TLB has started the development of the AI server low-power memory module specification SOCAMM3 substrate with two large domestic and foreign storage manufacturers. According to industry news on the 22nd, TLB recently began negotiating design directions and manufacturing processes with two existing storage customers, with specifications yet to be determined. The storage manufacturers plan to complete the design with the substrate manufacturers before finalizing the specifications, with actual substrate mass production expected in 2028.SOCAMM will make the low-power DRAM for mobile devices into a detachable AI server module, which will be first used by NVIDIA in its own AI servers. SOCAMM2 has developed into a JEDEC standard. Currently, each NVIDIA Vera CPU is equipped with 8 SOCAMM2 based on LPDDR5X, with a maximum capacity of 1.5TB per CPU and a bandwidth of 1.2TB per second. The SOCAMM2 substrate requires five layers, and the number of layers for SOCAMM3 will increase, raising the difficulty of processes such as micro-hole processing. The applicable systems have not been disclosed, and the mass production timeline in 2028 coincides with NVIDIA's roadmap. NVIDIA announced in March this year at GTC that it will launch the Feynman GPU and the new CPU Rosa in 2028.TLB's SOCAMM2 substrate has already entered mass production earlier this year, with production volume and sales in the second quarter increasing more than three times compared to the previous quarter. The production time and unit price are both about twice that of existing module substrates. The industry expects TLB's SOCAMM substrate sales to be around 70 billion Korean won this year, and about 140 billion Korean won next year. Brokers estimate the company's overall revenue this year to be about 350 billion to 370 billion Korean won, a year-on-year increase of about 40%, and around 450 billion Korean won next year. The company will begin construction of its second factory next month in the An Phu Industrial Park in Bac Ninh Province, Vietnam, aiming for production in the fourth quarter of next year, but SOCAMM substrates will still only be produced at the headquarters in Ansan in the short term.

first_img Cardano founder is developing a new programming language that integrates transaction intentions with Groth16 proofs

According to CoinDesk, Cardano founder Charles Hoskinson stated that he is personally developing a new programming language for the Cardano network and collaborating with Input Output Global engineer Lucas Rosa. Lucas Rosa is a co-creator of Aiken, one of Cardano's main smart contract languages. Hoskinson posted on the X platform that this will be the first programming language to combine user transaction completion intentions with cryptographic proofs.It is reported that the language will support three tools: transaction intent, data with carry proofs, and Groth16 proofs. Transaction intent allows users to specify the goals they want to achieve, while leaving the specific steps to the application, for example, traders can request that a token be exchanged for another only when the final price meets certain conditions, without specifying the exact path.Data with carry proofs can attach cryptographic evidence to information, proving that it complies with specific rules, allowing other applications to verify without recalculating. Groth16 is a widely used proof system that can generate small-sized, fast-verifying proofs, demonstrating that conditions have been met without exposing private data.With the combination of the three, developers can build applications that coordinate transactions, check complex conditions, and pass verified information between services, ultimately verified by Cardano with a small proof. Currently, Cardano developers mainly use Haskell-based Plutus and Aiken to write smart contracts, while Input Output Global, founded by Hoskinson, has its own privacy blockchain Midnight with its language Compact. The name, public code, and release date of this new language have not yet been announced.
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