Bad news for fans of cardboard VR headsets, though.

Macworld Tariffs! Are they off? Are they on? Are they halved, doubled, super-tricksy inside-outified? Nobody really knows. While my colleague Jason Snell already gave some concrete ideas for how Apple can deal with the wild world of import taxes, I’m taking a more speculative bent. Specifically, how could tariffs be an opportunity for Apple? That seems counterintuitive, I know, but hear me out: not everything Apple does needs to be directly impacted by whatever the administration of the United States decides to do this particular week/day/hour. And given the current volatile situation Apple (and the rest of the economy) finds itself in, maybe taking a beat to try and find the silver lining wouldn’t be such a bad thing after all. It’s the software, stupid What makes Apple primarily vulnerable to these capricious tariffs is that a large part of its business is in hardware–physical goods that need to be moved from one place to another. The good news is that software largely isn’t affected by tariffs. Which in and of itself makes it a good area to prioritize. Of course, Apple isn’t just a hardware company: it also builds the software for (primarily) its products, which makes it unusual amongst many of its competitors. And the good news is that software largely isn’t affected by tariffs. Which in and of itself makes it a good area to prioritize, just as the company is surely doing for its likewise unaffected Services division. Moreover, the broad consensus among watchers of the company over the last several years has been that Apple’s software often feels like it’s lagging behind its incredibly successful hardware. So maybe the timing is perfect for Apple to spend a little more time focusing on software. There are any number of projects that could use some more attention, and not only can Apple afford to spend the time, but it can also benefit the company long term. Those lovely intangibits There are two types of software challenges that Apple could undertake. The first are the big swings: things like Apple Intelligence’s overhaul of Siri. These are the opportunities for the company to go back to the drawing board, to take a hard look at a feature and maybe rework it from the ground up, something that reports indicate is already happening with the voice assistant. The second is the kind of smaller problems that plague software in general, and certainly Apple’s software in specific: bugs, inconsistencies, design improvements, and enhancements. While these may not be showstoppers or big marquee features, they are the kind of quality-of-life problems that impact users every day. Imagine what Apple could do with iPadOS if it decided to pay full attention to it.Apple What kind of benefit does this accrue to Apple? Well, the bottom line is that improving its software makes its users happier and that, in turn, leads to both repeat customers as well as making said same customers more likely to extol the products’ virtues to others. It’s a long play, to be sure, but Apple has never shied away from taking the long-term view. And, just to be clear, I’m not simply advocating throwing more people at Apple’s software problems–a strategy that’s largely been shown to be ineffective–and, more to the point, I’m certainly not saying that the people building hardware for Apple’s devices are the same people building hardware and that you can just swap them out. No, this is ultimately a matter of institutional attention. We’ve all seen the endless videos and ads of Apple showing off every curve and smallest element of its hardware. I’m simply asking what would happen if it lavished the same amount of love, attention, and pride on every infinitesimal design details of its software. Hardware that lives long and prospers This focus on software has other long-term benefits, too. While Apple obviously doesn’t want to cede sales of its new devices if it can help it, it’s already been contending with another challenge in this arena for some time: whether its hardware is too good. With Apple’s devices generally retaining their utility longer than many of its competitors, upgrade cycles have lengthened. Part of that is from Apple’s own efforts: its latest software releases tend to reach fairly far back. Last year’s iOS 18 runs on devices back to 2018’s iPhone XR/XS, which were also the oldest phones supported by iOS 17. Especially in the era of Apple Silicon-based Macs, we haven’t really ended up with a situation where new software or capabilities won’t run on a recent Mac, and Apple’s even maintained backwards compatibility with late Intel Macs for some time. So, arguably, improving software pays dividends for Apple because it’s immediately applicable to a large number of their devices and users. But making software better and better also has a counterweight approach when it comes to Apple’s hardware. If Apple does have to raise the prices of its hardware, providing a better software experience is another pro in the column when it comes time for people to decide whether or not they’re going to buy. “Sure, Apple’s products are expensive, but how could I use anything else?”

Enterprise users are receiving unprovoked offers to upgrade to Windows 11 due to an issue in Microsoft’s device management tools. The issue is with Microsoft’s Intune software, which typically allows system administrators to manage mobile devices. Intune governs the use of Windows, macOS, and Android devices attached to enterprise environments. The news was first reported by Bleeping Computer, which also noted that a code-fix was being deployed to systems. The problem was a “latent code issue,” which was then being fixed. It is unclear how Windows systems were offered upgrades to Windows 11. Intune typically determines the usage policies for devices and provisions hardware and software updates. Microsoft didn’t respond to questions from Computerworld about the issue or when it would be resolved. An advisory posted on Microsoft’s website also said system administrators must manually roll back updates to Windows 11 caused by the bug. Enterprises should also pause Windows Updates. Device management can be a problem if system administrators don’t have proper controls, or if rollouts are not compatible with the device management tools in place, said Jack Gold, principal analyst at J. Gold Associates. One example is rolling out a driver update that may not work or has zero capability to roll it back, like what happened with Crowdstrike, Gold said. The rollback from Windows 11 due to the Intune glitch should be tested on a limited basis and rolled out to the entire fleet of affected systems after ensuring no glitches show up, Gold said. The unintentional upgrade notices to Windows 11 come despite Microsoft’s recent announcement of automation and AI features for Intune, designed to automate device management and patch updates. Last month, the company announced Security Copilot, which is designed to prevent security attacks. A Copilot feature for Intune called “Vulnerability Remediation Agent” makes it easier to prioritize patch management and the remediation of security issues on devices. Microsoft last month also announced the preview of Windows 365 Frontline, which sets up a temporary “shared mode” for the cloud-based OS. An Intune automated feature prepares the virtual PC policies that include app access and other provisions. Gold said security and patch management, which is already unforgiving for sysadmins, will get even tougher with AI deployments on devices. Beyond managing devices, system administrators will need to take charge on managing the bits of data accessible to enterprise users for AI. “For the most part, rolling out AI should be no more difficult than other apps, but we’ve seen in the past that apps don’t always play nice together on the same system,” Gold said.

After the U.S. government initially cut its funding of the CVE database, used to track security vulnerabilities in operating systems and software, CISA has said it will continue to be funded for another 11 months at least.The loss of CVE will make it harder to track malwareEarly on Wednesday, it was reported that the Common Vulnerabilities and Exposures (CVE) database had its funding cut. Within hours, its funding has been restored for just under one more year.The CVE is an important part of modern cyber security. It's a central database of vulnerabilities found in operating systems and applications, which can be abused by hackers and malware to attack targets in various ways. Continue Reading on AppleInsider | Discuss on our Forums

Frostpunk 2 launched for PC last September, and is set to release for PS5 and Xbox Series X/S later this year, but 11 bit studios has still more Frostpunk in the pipeline for the not-too-distant future, the Polish studio has announced. In the studio’s latest quarterly earnings report, 11 bit studios president Przemysław Marszał revealed that the company is planning on releasing a new title in the Frostpunk universe at some point in 2027, with production already underway. That’s in addition to a separate, larger-scale project targeting a 2029 release, which doesn’t seem to be related to Frostpunk. “As a developer and publisher, we intend to diversify both the scale of our projects and their development timelines,” Marszał said. “This also means capitalising on near-term, financially attractive opportunities. This underlies our decision to initiate production on the next project set in the Frostpunk universe, which we plan to launch as early as 2027. In parallel, we have commenced development on a larger-scale title, targeted for release in 2029.” Presumably, the Frostpunk game will be a relatively smaller-scale title, considering the fact Frostpunk 2 only just launched. What that will mean in precise terms remains to be seen, but fans of 11 bit’s post-apocalyptic city building survival franchise certainly have reason to be excited. Frostpunk 2 is available on PC, and has sold over 511,000 copies as of last November. The game launches for PS5 and Xbox Series X/S sometime this Summer.

html PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN" "http://www.w3.org/TR/REC-html40/loose.dtd" Autodesk’s new marketing image for Flame from its social media profiles, created by Rodeo FX. Autodesk has begun its Flame 2026 release cycle, the latest annual series of updates to Flame, its compositing, finishing and effects softwareFlame 2026 introduces support for OpenColorIO color management, a new Type tool for on-screen text, and an AI-based clip upscaling system. Flare and Flame Assist, the cut-down editions of Flame, are also listed in the release notes, but another former Flame Family application, color grading tool Lustre, has been discontinued. OpenColorIO replaces SynColor for color management The headline change in Flame 2026 is support for open color-management standard OpenColorIO (OCIO), replacing the old Autodesk Color Management (SynColor) system.The move brings Flame in line with Autodesk’s other Media and Entertainment products like Maya and 3ds Max, and with other key tools in visual effects pipelines like Houdini and Nuke. The change is backwards-compatible, with projects created in previous versions of Flame being automatically migrated from SynColor to OCIO. New Type tool replaces the old Text tool Other new features include the new Type tool for on-screen text and titles, replacing the legacy Text tool.According to Autodesk, it provides better workflows for kerning and styling text, better font management, and supports layer management via the Layers list. The Type tool also integrates better into Batch, Batch FX and the Timeline than its predecessor, including support for color management, context views, and multiview. Machine-learning-based video upscaling Flame 2026 also introduces a new machine learning upscaling system, available in the Media Export window, and via the Resize & Crop, Render and Write File nodes.Clips can be increased in resolution by 2x, 3x or 4x; and upscaling can be performed on CPU or GPU, although GPU processing is restricted to clips with resolutions of 1,920 x 1,080px or less. The option is currently only available on Rocky Linux, and does not support background export. There are also new AI models for the Morph and Timewarp tools, which both got new machine-learning-based capabilities during the Flame 2025 updates. Other workflow and pipeline improvements Workflow improvements include a redesigned Project Management window, and new options to set file locations for a project.There are also smaller updates to Batch and Batch FX, and the MediaHub. The Input and Output Clip modules have been deprecated. You can find a complete list of changes, including updates to the Python API and the RAW file formats supported, via the link at the foot of this story. Other Flame Family applications: Lustre discontinued after Lustre 2025 In related news, the Flame Family of applications has shrunk with this release, color grading tool Lustre having been discontinued after the release of Lustre 2025.The other two members of the family, Flare and Flame Assist – both cut-down versions of Flame itself – are still listed in the release notes. Pricing and availability Flame 2026 is compatible with Rocky Linux 8.10/9.3/9.5 and macOS 13.0+.The software is available rental-only. Subscriptions cost $640/month, up $40/year since the previous release, or $5,045/year, up $175/year. We’ve contacted Autodesk to check the current subscription prices of Flare and Flame Assist, and will update if we hear back. Read a full list of new features in Flame 2026 in the online documentation Have your say on this story by following CG Channel on Facebook, Instagram and X (formerly Twitter). As well as being able to comment on stories, followers of our social media accounts can see videos we don’t post on the site itself, including making-ofs for the latest VFX movies, animations, games cinematics and motion graphics projects.

Go to any run club in the world and there’s a good chance that everyone there has two things: a Garmin smartwatch to track their run and a Strava account to brag about it. Given the global running boom, it makes Strava’s lack of any modern, in-app training plans a curious and glaring omission. Or, at least, it was until today as Strava is acquiring Runna.For those who don’t torture themselves with a 6AM daily run, this is big news — even if the companies are keeping mum on the deal’s financial details. Strava is the most well-known fitness social media app on the market. Meanwhile, Runna burst onto the scene in 2021 and has quickly climbed the app charts for folks in need of 5K, 10K, or marathon training plans. Since launch, it’s secured an additional $6.3 million in funding for its AI-powered run coaching, with users spanning 180 countries. In 2024, Runna also tripled the size of its team and is currently hiring roughly 50 roles to expand the product and tech. Peruse running subreddits or RunTok, and you’ll invariably see someone asking about or recommending the app.The deal seems like a win-win for Strava and Runna. Strava gets to shore up one of its biggest weaknesses — the lack of running training plans. For Runna, it gets access to one of the largest online running communities and Strava’s coffers.Related“For a while, Strava had created static, document-based plans for runners but the reality is those were used very, very infrequently,” Strava CEO Michael Martin says. According to the company’s research, the lack of guidance was a pain point for longtime users and newcomers to the app. “We came to realize that, as it related to runners, that guidance was training plans.”There’ll be a short wait before Strava and Runna users see changes from the acquisition.“Effectively, nothing changes for the user out of the gate. Our plan with this acquisition is to invest further into growing the Runna app, invest in the Runna team, and then continue to operate them as independent but in an integrated fashion,” Martin says, adding that once the deal is fully wrapped, users can expect to start seeing changes in the coming weeks and months.“The ambition is to do things where it makes sense,” adds Runna cofounder and CEO Dom Maskell, who notes a more seamless integration between the two apps would help create a smoother user experience. “It’s like, the user comes on and they want to see what run they’re doing today. That sits in Runna, and then they want to go find a route for that run — that sits in Strava. Then, if they want live coaching, that’s on Runna and then Strava frankly has better tech than us for recording on your phone. At the moment, the user kind of gets passed off quite a lot of times.”“…I genuinely believe this is an amazing thing for all users. I’m happy to tell everyone about it and sit on Reddit for the whole day to answer everyone’s questions.”One thing that hasn’t been decided yet is how subscriptions will work. Strava has a free tier but charges $79.99 a year for premium features, while Runna costs $119.99 annually. While Runna currently uses Strava’s third-party API, until the details are hammered out, users will still need to subscribe to both services to get the full range of features. When pressed further on the issue, Martin says he envisions the Runna acquisition to be more akin to when the company bought Recover Athletics, a prehab and injury prevention app, than when it acquired FATMAP, a 3D-mapping platform. With a Strava subscription, Recover Athletics is essentially a free perk but functions as a separate app. FATMAP’s app, however, was retired in late 2024 and its tech/features were incorporated into Strava.Subscriptions will be a thorny issue for both Strava and Runna users. Over the past few years, the r/Strava subreddit has been rife with accusations of enshittification, with many directing their ire toward the app paywalling features. Generally, users tend to react badly to any changes in subscriptions or smaller brands getting gobbled up by bigger ones. Case in point, in 2023, Strava hiked up subscription prices in a messy rollout that left users angry and confused. You only need to look at the reaction to Garmin’s recent subscription launch to know the Strava-Runna news may not go over well with some users — a fact Martin and Maskell are well aware of.“We’ve got quite an active Reddit community, and I know there’s probably quite a large overlap between them and the strong voices in the comment section,” says Maskell. “We try to be very transparent and open with them, and I genuinely believe this is an amazing thing for all users. I’m happy to tell everyone about it and sit on Reddit for the whole day to answer everyone’s questions.”“I’d be lying to not say it’s a challenge to think about investing in growth during a period such as this, but it’s so clearly the right thing to do,” Martin says, referring to the current uncertain economic climate. “This is very much a growth and investment play. This isn’t an efficiency play.”See More:

The Challenge of Data Selection in LLM Pretraining Developing large language models entails substantial computational investment, especially when experimenting with alternative pretraining corpora. Comparing datasets at full scale—on the order of billions of parameters and hundreds of billions of tokens—can consume hundreds of thousands of GPU hours per run. Consequently, practitioners resort to smaller‐scale experiments as proxies for large‐model behavior. Yet these “pilot” studies are rarely published, producing a fragmented landscape in which each laboratory repeats similar small‐scale tests without shared benchmarks or methodologies . This opacity impedes reproducibility, underutilizes collective insights, and obscures the true trade‑offs between development compute and final model performance. DataDecide To address these limitations, the Allen Institute for AI (AI2), in collaboration with the University of Washington and the University of Pennsylvania, today releases DataDecide—a comprehensive suite of controlled pretraining experiments spanning 25 distinct corpora and 14 model sizes from 4 million to 1 billion parameters. DataDecide’s datasets include well‑known sources such as Dolma, DCLM, RefinedWeb, C4, and FineWeb, alongside variations produced by domain ablation, deduplication, quality filtering, and source mixing. Each model is trained at a fixed token‑to‑parameter ratio of 100 (100 tokens per parameter), reflecting the “overtraining” regime that optimizes inference efficiency. In total, over 1,050 models and more than 30,000 checkpoints—each evaluated across ten downstream tasks—are released to the public. Technical Structure and Pragmatic Benefits DataDecide orchestrates experiments along three axes: Data Recipes: Twenty‑five well‑documented pretraining corpora, each embodying different curation strategies (see Table 1 in the paper for full recipe specifications) . Model Scale: Fourteen parameter configurations (4 M–1 B), programmatically derived via the OLMo model ladder to ensure consistent training hyperparameters across scales. Each non‑target scale includes two “early‑stop” seed runs, while the 1 B‑parameter models feature three complete seed reruns to quantify variability. Evaluation Suite: The OLMES benchmark of ten multiple‑choice tasks (e.g., MMLU, ARC Easy/Challenge, HellaSwag, MBPP, HumanEval) provides a multifaceted view of language understanding, commonsense reasoning, and code generation performance. By releasing both pretraining datasets and corresponding models, DataDecide enables researchers to: Reuse checkpoints for new evaluations without retraining. Experiment with novel prediction methods (e.g., advanced scaling‑law fits, smoothing techniques). Investigate benchmark sensitivity to training data and model scale. Key Findings and Quantitative Insights DataDecide’s systematic analysis yields four practical guidelines: Single‑Scale Baseline Robustness: Ranking corpora by downstream accuracy at a single, small scale (e.g., 150 M parameters) achieves ~80 percent decision accuracy for predicting the best dataset at the 1 B‑parameter target scale. In contrast, eight baseline scaling‑law extrapolations do not surpass this simple heuristic, underscoring its cost‑effectiveness. Task‑Dependent Compute Sensitivity: The compute budget required for reliable decisions varies markedly by task. Benchmarks like MMLU and ARC Easy become predictable with less than 0.01 percent of the target compute, whereas HellaSwag and SocialIQA demand orders of magnitude more FLOPs to achieve similar decision accuracy . Proxy Metric Selection: Continuous likelihood metrics—specifically the character‑normalized average probability of correct continuations (CORRECT PROB) and total probability (TOTAL PROB)—outperform discrete accuracy measures at small scales. This is most pronounced on code tasks (MBPP, HumanEval), where decision accuracy jumps from near‑random to over 80 percent with CORRECT PROB as the proxy . Variance and Spread Considerations: High decision accuracy correlates with low run‑to‑run variance (noise) and ample performance spread across datasets. Proxy metrics that reduce noise or amplify spread thus directly enhance prediction reliability. Concluding Perspective DataDecide transforms pretraining data selection from an ad hoc art into a transparent, data‐driven science. By open‑sourcing all 25 corpora, 1,050 models, 30,000+ checkpoints, and evaluation scripts on Hugging Face and GitHub, AI2 invites the community to reproduce findings, extend evaluations to new benchmarks, and innovate on decision‑making methods. As LLM development continues to demand ever‑greater compute resources, DataDecide offers a principled framework for minimizing wasted experiments and maximizing insight—paving the way toward more efficient, reproducible, and collaborative AI research. Check out the Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 90k+ ML SubReddit. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/OpenAI Releases Codex CLI: An Open-Source Local Coding Agent that Turns Natural Language into Working CodeAsif Razzaqhttps://www.marktechpost.com/author/6flvq/SQL-R1: A Reinforcement Learning-based NL2SQL Model that Outperforms Larger Systems in Complex Queries with Transparent and Accurate SQL GenerationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/THUDM Releases GLM 4: A 32B Parameter Model Competing Head-to-Head with GPT-4o and DeepSeek-V3

Author(s): Igor Novikov Originally published on Towards AI. Image by the author Crew AI is one of the most popular agentic AI frameworks out there. I’ve reviewed Autogen by Microsoft before, and it turned out to be quite buggy. This one is more mature, better documentation and generally much more stable. Architecturally they are all kinda the same though. The basic idea is a tree of execution where you have agents as nodes of the tree that can have one or both ways connections to other agents. That allows them to build various types of flows with them to solve different tasks. Let’s look at Crew basic structure: So we have: Agents: The overworked interns of your AI crew. Each one is programmed with a specific role (like “Cat Meme Analyst” or “Code Reviewer”) and a set of tools. Pro tip: Set verbose=True to hear them complain about their workload in real time. Tasks: The to-do list you slap onto your agents. “Generate 10 puns about quantum computing” or “Debug this code while pretending you’re a pirate.” Tasks can be stacked and prioritized. Processes: The “how” of chaos coordination. Crew AI offers options like Hierarchical (CEO agent barks orders at manager agents, who panic-delegate to intern agents) or Sequential (tasks move like a conga line at a retirement home). Choose wisely, unless you want your “Generate cat memes” task stuck behind “Solve climate change.” Tools: The Swiss Army knives your agent. Need to call an API, scrape the web, or send a passive-aggressive Slack message? Tools let your agents do it. Let’s build something like a marketing research system. You tell it the name of the company and it will find out all about it and its decision makers. We are going to use Google Collab for easy sharing. The Crew AI framework enables the creation of autonomous agent teams to tackle complex workflows. In this marketing research system, two crews collaborate hierarchically to generate a comprehensive company report. First let’s ask DeepSeek to create a plan for such a system: Environment Setup Install necessary packages: crewai, openai, langchain, crewai-tools, gdown, and selenium. Configure Selenium with headless Chromium (including cookie management and user-agent spoofing). Tool and Utility Preparation Set up web scraping and search tools (e.g., SerperDevTool, WebsiteSearchTool, ScrapeWebsiteTool). Create utilities for human-like delays, caching scraped HTML, and parsing HTML with an LLM. Agent Development Company Analyst: Gather general company data (industry, specialization, recent events) using web search and scraping tools. Case Studies Analyst: Retrieve relevant case studies from an internal repository (Google Doc) using semantic search. Decision Maker Analyst: Extract key executive profiles from LinkedIn using a Selenium-driven scraper. Manager Agent (Sales Strategist): Delegate tasks to the analysts, consolidate their findings, and produce a cohesive markdown report. Task Definition Define tasks with clear instructions (e.g., “Research the company ‘X’” and “Identify decision-makers”). Set expected output format (markdown report without code blocks). Crew Orchestration Organize agents into a hierarchical crew using Crew AI. Assign tasks to appropriate agents and set the process flow (manager oversees delegation). Initiate the crew with company input and execute the workflow. Integration and Testing Integrate LLM-based HTML extraction to structure raw data. Implement caching to reduce redundant scraping. Test the system against real-world data (e.g., LinkedIn pages) and adjust for dynamic content loading. Finalization Consolidate the outputs from all agents into a final report. Validate data accuracy and adjust error handling or delays as needed. Deploy or extend the system for further marketing research tasks. Now that we have a plan, let’s write the code. web_search_tool = SerperDevTool()website_scrape_tool = ScrapeWebsiteTool()case_studies_tool = GetOurCompanyCaseStudies() inputs = { 'company': "Microsoft"} company_researcher = Agent( role="Company Analyst", goal="Analyze the company, gather data by reviewing its social media and website, and identify pain points.", backstory=f"You are an experienced analyst with expertise in business and process automation at {OUR_COMPANY}. Your task is to gather information that the sales department will use to develop hypotheses for client outreach.", tools=[web_search_tool, website_scrape_tool], verbose=True, allow_delegation=False, llm=llm) case_studies_researcher = Agent( role="Case Studies Analyst", goal="Analyze the company, gather data by reviewing its social media and website, identify pain points, and determine the most relevant case studies we can present to the company.", backstory=f"You are an experienced analyst with expertise in business and process automation at {OUR_COMPANY}. Your task is to gather information and select relevant case studies, which the sales department will use to develop hypotheses for outreach to clients.", tools=[web_search_tool, case_studies_tool, website_scrape_tool], verbose=True, allow_delegation=True, llm=llm) decision_makers_researcher = Agent( role="Decision Maker Analyst", goal="Analyze the key figures of the company, their interests, communication style, and level of technical knowledge.", backstory=f"A specialist in analyzing people and decision-makers, skilled in identifying the motivation of business leaders. You work at {OUR_COMPANY}. Your task is to gather information (company activities, recent social media posts, hobbies, place of residence) about the executives of potential clients, which the sales department will use to develop hypotheses for outreach to clients.", tools=[website_scrape_tool, web_search_tool], verbose=True, allow_delegation=False, llm=llm) manager = Agent( role="Sales Strategist", goal="Manager of a team that researches a company and creates detailed reports based on data analysis and research findings about potential clients for sales team to use for their outreach strategy.", backstory="Efficiently manage the crew and ensure high-quality task completion. You're known for your ability to turn complex data into clear and concise reports, making it easy for others to understand and act on the information you provide.", verbose=True, allow_delegation=True, llm=llm_o1)task_research_company = Task( description=( "Research the company '{company}' and gather information about: industry, specialization, and recent events.\n" "The company name is exactly as it is spelled (including any special characters)—make sure you don’t mix it up with similarly named companies.\n" "Make sure to analyze their LinkedIn and website.\n" "Find similar cases from our Case Studies and mention them.\n" "Don’t draw any conclusions—just gather data into a report.\n" f"Today is {today}." ), expected_output="A fully fledge report with the mains topics, each with a full section of information. Formatted as markdown without '```'" ) sales_research_crew = Crew( agents=[company_researcher, decision_makers_researcher, case_studies_researcher], tasks=[task_research_company], verbose=True, memory=True, manager_agent=manager, process=Process.hierarchical) company_research_results = sales_research_crew.kickoff(inputs=inputs) print(company_research_results) The full version is here: Google Colab Edit description colab.research.google.com Let’s see how the code works. Company Analyst Crew The Company Analyst dives into the company’s online presence — analyzing its website and social media — to extract industry trends, recent events, and general insights. The Case Studies Analyst searches our database of case studies (downloaded from Google Docs!) to identify previous cases that might be relevant to the target company. The Decision Maker Analyst focuses on gathering details about the company’s key figures, their interests, and other subtle hints that could be useful for tailoring outreach. The Sales Strategist (Manager) serves as the orchestration engine that coordinates the flow of tasks, ensuring that data from each agent is well-structured and that nothing slips through the cracks. The LinkedIn-Focused Decision Makers Crew The LinkedIn Researcher uses our custom GetCompanyLinkedinPeople tool to scrape LinkedIn pages, carefully handling authentication via cookies and simulating human behavior with Selenium. The Data Checker validates and cross-references the information extracted from LinkedIn and other sources, helping to ensure that the final report is reliable. How the System Works When you call the kickoff method on the crew with an input like { ‘company’: ‘Microsoft’ }, the system follows a hierarchical process. The manager agent delegates tasks to specialized agents, each leveraging its own toolkit (from web search and website scraping to semantic search within our case studies) to complete their assignment. The result? A fully-fledged, markdown-formatted report. Our use of the Process.hierarchical mode ensures that the Sales Strategist (Manager) not only delegates but also synthesizes the outputs from each subordinate agent. This hierarchical workflow minimizes randomness in agents actions. With multiple agents processing data in parallel, ensuring the quality of information is crucial. The inclusion of a Data Checker agent in the second crew helps verify the integrity of the information. Tools CrewAI comes with a set of pre-build tools that cover all basic needs, but you can add your own as I did with LinkedIn parser. Each agent should be equipped with an adequate set of tools for the task. For example: Web Search and Scraping — Agents like the Company Analyst and Decision Maker Analyst use tools such as SerperDevTool and ScrapeWebsiteTool to navigate and extract data from the web. LinkedIn Scraping — Our custom GetCompanyLinkedinPeople tool, built on Selenium, mimics human browsing patterns to deal with LinkedIn’s dynamic content and login requirements. Text Extraction from Google Docs — The GetOurCompanyCaseStudies tool downloads and caches our case studies, allowing semantic search over a well-maintained repository. Conclusions Crew is a pretty good framework. It is stable and straightforward. It abstracts enough of boilerplate code but still gives you fine grained control. I find it quite easy to understand and learn. As for the agentic approach — if you run the code you will see that results are not perfect. As agentic logic is fuzzy, and not rigid like a computer program, it provides different results each time, sometimes wrong or hallucinations. On average though — it works pretty well, well enough to be useful in cases where exact correctness is not a requirement. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI

For a limited time, Amazon is offering a Hoto 3.6V Electric Screwdriver for $35.99 $29.99 after you clip the 25% off coupon on the produce page and apply coupon code "508DQAW9" during checkout. This is a great, compact screwdriver for anyone who tinkers with small electronics and deals with dozens of tiny screws, but wants something stronger than one of those "mini precision" screwdrivers.Update: Another $6 price drop compared to last week.40% Off Hoto 3.6V 4Nm Electric Driver for $29.99Also Clip the 25% Off Coupon on the Product PageHoto 3.6V 4Nm Electric ScrewdriverHoto sells a few electric screwdriver models, and this is their most powerful model. Most of their electric screwdrivers are very small - about the thickness of a pen flashlight - and deliver about 0.2Nm-0.5Nm of max torque. This particular model feels heftier - about the thickness of a regular flashlight - and delivers up to 4Nm of max torque. It also has a higher rotational speed of 220RPM (vs 150RPM on the smaller models) and a far bigger 1,500MaH battery (vs 300mAh or less on the smaller models). It's more versatile since it will handle bigger and tougher jobs, however that means it's also powerful enough to strip screws, so you'll have to be more careful.Although this screwdriver isn't the smallest, it's still compact and weighs in at just over half a pound. It fits neatly in the provided aluminum box, which also houses a selection of 12 different bits. In case you lose any and need replacements, the screwdriver is compatible with standard sized bits. There's even a handy LED ring to illuminate whatever it is you're driving and it will recharge over USB Type-C.Why Should You Trust IGN's Deals Team?IGN's deals team has a combined 30+ years of experience finding the best discounts in gaming, tech, and just about every other category. We don't try to trick our readers into buying things they don't need at prices that aren't worth buying something at. Our ultimate goal is to surface the best possible deals from brands we trust and our editorial team has personal experience with. You can check out our deals standards here for more information on our process, or keep up with the latest deals we find on IGN's Deals account on Twitter.Eric Song is the IGN commerce manager in charge of finding the best gaming and tech deals every day. When Eric isn't hunting for deals for other people at work, he's hunting for deals for himself during his free time.