Latest news with #GPT-4Turbo
Yahoo
27-05-2025
- Business
- Yahoo
One App With All of the AI Models You Actually Want to Use
The following content is brought to you by PCMag partners. If you buy a product featured here, we may earn an affiliate commission or other compensation. If you rely on AI for work, then you've likely noticed that many of the free tools are either inefficient or extremely limited in scope. Even OpenAI puts a cap on how much you can use GPT-4, and their paid subscriptions aren't cheap. The alternative is to get access to the same AI models through a different platform. That's what does. This all-in-one AI platform gives you access to GPT-5 and GPT-4 Turbo, Gemini, Claude, and more. It's also only $29.97 (reg. $234). puts all the AI tools you rely on into one platform. You can generate copy with GPT, craft images with Midjourney, and there's even AI for editing audio and video It works on a credit system, but the lifetime subscription gives you more than enough. With your 1,000,000 monthly credits, you could generate over 800,000 words, research 1,933 SEO keywords, upscale 241 images, convert 120,833 characters to speech, or transcribe up to 4,833 seconds of audio. These credits roll over every month if you don't use them all. And just by logging in daily, you can earn up to 450,000 additional credits every month. Don't rent tools you can own. Get a subscription to while it's on sale for only $29.97. Prices subject to change. PCMag editors select and review products independently. If you buy through StackSocial affiliate links, we may earn commissions, which help support our testing.
Entrepreneur
20-05-2025
- Business
- Entrepreneur
Why Are Some Business Owners Ditching OpenAI for This Less Expensive Tool?
Disclosure: Our goal is to feature products and services that we think you'll find interesting and useful. If you purchase them, Entrepreneur may get a small share of the revenue from the sale from our commerce partners. Artificial intelligence (AI) has fast become an essential part of modern business. However, these resources have been more difficult to come by for small businesses and individual ventures. Smaller companies just don't have the budget to purchase enterprise-grade access to AI tools individually, but that doesn't mean they need to stick to free OpenAI memberships and other AI models that waste more time than they save. Instead, try This all-in-one AI platform gives you access to the same AI models that are used in the high-end subscriptions: GPT-4 Turbo, Gemini Pro 1.5, and MistralAI, among others. You can use these models to generate copy, research SEO, transcribe audio, and perform a myriad of other tasks. And it's only $79.97 for a lifetime subscription, but only for a limited time. What can you do with a subscription? 1min gives you a multitude of popular AI models that can integrate into workflows across industries. Generate copy using GPT-4 and GPT 4o, craft stunning visuals with Midjourney, or even use AI to summarize PDF documents. Need to make a social post? You can use 1min to create a video, write a script, then convert that script to speech. This AI platform operates on a credit system. Your plan gives you 4 million credits a month. That's the equivalent to more than 1 million words per month, nearly 6,000 SEO keywords, 1,186 images, removing 74 image backgrounds, or generating 37 videos. And you can even earn an additional 450,000 just from logging in every day. Plus, unused credits roll over. This plan can be shared by up to 20 members on a team, so your whole company can benefit from a little AI assistance. Equip your business with AI tools that will help you grow. Get a Advanced Business Plan lifetime subscription on sale for $$79.97 (reg. $540). That's over 80% off, but only for a little while longer. Advanced Business Plan Lifetime Subscription See Deal StackSocial prices subject to change.
India.com
18-05-2025
- Business
- India.com
The New Age of Intelligent Quality Assurance
Modern software assurance sits at an unusual crossroads. Business leaders want every release to reach customers faster, regulators demand airtight security, and users expect flawless experiences across web, mobile, and cloud. Traditional quality-control techniques—manual regression passes, siloed load tests, overnight batch jobs—cannot keep pace with this tri-axial pressure. What is emerging instead is a discipline that blends advanced automation, AI-augmented analytics, and, increasingly, specialized hardware such as quantum annealers to expose defects before they ever reach production. Recent Research Findings Three peer-reviewed studies help illuminate where this discipline is heading. 'AI/ML Algorithms for Phishing Detection and Automated Response Systems in Cloud-Based Email Security,' authored by Akhil Reddy Bairi and published in Advances in Deep Learning Techniques in February 2023 , shows how transformer-based models ingest sender reputation, content cues, and contextual signals to quarantine fraudulent messages in real time—moving well beyond the static rule sets that dominated earlier secure-email gateways. and published in Advances in Deep Learning Techniques in , shows how transformer-based models ingest sender reputation, content cues, and contextual signals to quarantine fraudulent messages in real time—moving well beyond the static rule sets that dominated earlier secure-email gateways. 'AI-Augmented Test Automation: Enhancing Test Execution with Generative AI and GPT-4 Turbo,' first-authored by Akhil Reddy Bairi in Journal of Artificial Intelligence General Science in February 2024 , extends that idea to the software-delivery pipeline itself. Here, large language models generate edge-case test paths, draft debugging hints, and adapt test data on the fly—shrinking release windows without loosening quality controls. in Journal of Artificial Intelligence General Science in , extends that idea to the software-delivery pipeline itself. Here, large language models generate edge-case test paths, draft debugging hints, and adapt test data on the fly—shrinking release windows without loosening quality controls. 'Unified Pipelines for Multi-Dimensional LLM Optimization Through SFT, RLHF, and DPO,' again led by Akhil Reddy Bairi and appearing in Journal of AI-Assisted Scientific Discovery in September 2024, tackles a different bottleneck: fine-tuning large language models for domain use. By chaining supervised fine-tuning, reinforcement learning from human feedback, and direct-preference optimization, the study delivers a single workflow that surfaces high-quality models with fewer compute cycles and tighter ethical guardrails. Though each paper targets a distinct layer—email security, test-execution speed, and model-optimization efficiency—they share two departures from prior art. First, the research treats automation not as a scripted checklist but as an adaptive, continuously learning system. Second, they all integrate directly with existing delivery platforms (Microsoft Defender, Cypress/Playwright pipelines, and cloud fine-tuning APIs, respectively), ensuring practical uptake rather than laboratory novelty. About Akhil Reddy Bairi These results are best understood in light of the author's professional trajectory. Akhil Reddy Bairi has spent eight years as a Software Development Engineer in Test (SDET) building and hardening automation frameworks for organisations whose revenues depend on fault-tolerant digital platforms. Most recently, at a major retailer, he led a Playwright-based framework that now covers significant portion of the retailer's backend data workflows, guarding more than $5 million in daily online sales. Earlier roles at BetterCloud, CVS Health, and Paycor saw him cut regression runtimes by as much as 75 percent, migrate legacy Selenium suites to lightweight Cypress stacks, and introduce Gatling-driven performance gates for micro-services running on GCP. There are two things Akhil tends to do no matter the project. One, he pushes testing as close as possible to where bugs usually start like right after code is committed, or at the API level, or even in a Kafka queue. That way, problems get spotted early. Two, he treats tools just like regular code. Everything's tracked in version control, dependencies are locked down, and teams can see what's going on at all times, just like with live apps. You can spot the same approach in his 2023–2024 work too, where stuff like model drift, uneven data, and system load aren't just side issues they're tackled like real engineering problems. Equally important is Akhil's habit of pairing new techniques with hands-on enablement. At BetterCloud he mentored junior SDETs through Cypress migration workshops; at Nelnet he trained manual QA analysts on Serilog-instrumented smoke suites; and in open-access venues he shares sample repos for integrating GPT-assisted test generation with existing CI pipelines. That community orientation is visible in the LLM-pipeline study, which adopts open-source fine-tuning APIs and publishes evaluation scripts under permissive licences to encourage replication and extension. Where Testing Meets Tomorrow Taken together, the three studies suggest a roadmap for organisations seeking resilience without sacrificing delivery velocity. Near-term, transformer-powered classifiers harden business-critical channels such as corporate email; mid-term, generative models curate exploratory test sets that traditional scripting misses; longer-term, unified optimisation pipelines render the upkeep of those very models cost-effective and auditable. The research also argues implicitly through field data and explicitly in cost-benefit sections that quality assurance is no longer a post-build gate but an AI-infused, continuously adaptive mesh spanning source control to customer inbox. For practitioners, Akhil Reddy Bairi's work illustrates that the boundary between engineering and research is growing thin. Novel algorithms must integrate with everyday delivery stacks, and production constraints should feed back into scholarly enquiry. For editors and technology leaders alike, that blend of rigour and real-world pragmatism may well define the next chapter of intelligent software assurance.
India Today
30-04-2025
- India Today
ChatGPT will no longer indulge in flattery and sycophancy, says OpenAI as it rolls back changes
OpenAI CEO Sam Altman acknowledged in a post on X on Monday that the recent ChatGPT-4o update has made the AI model's personality a bit 'sycophant-y and annoying'. Altman promised a fix soon. On Wednesday, the OpenAI CEO shared a fix: it is rolling back the update. Altman says that the update has been 100 per cent rolled back for ChatGPT free users, and will be rolled back completely for free users soon. He says as soon as the roll back is complete, a new update will be addition to fixing the 'annoying' behaviour of the model, OpenAI will soon also be rolling out some other updates to the GPT-4o's personality. The details, however, will be shared in a few days. 'We started rolling back the latest update to GPT-4o last night. It's now 100% rolled back for free users and we'll update again when it's finished for paid users, hopefully later today. We're working on additional fixes to model personality and will share more in the coming days,' Altman wrote in a post on X. Earlier this week, when Sam Altman acknowledged the new annoying personality of ChatGPT-4o model, he ended up teasing that the AI model may soon get the capability to switch personalities. He revealed this in response to a comment on his post on X where a user asked if they will soon be able to change the personality of the AI chatbot, or if 'old and new [personalities can be] distinguished somehow?' Altman responded saying, 'yeah eventually we clearly need to be able to offer multiple options'.advertisement When Altman wrote in his post today that 'we're working on additional fixes to model personality' could be hinting at an option to switch between personalities? Maybe. We don't know. But we will soon know about it in the 'coming days'.OpenAI rolled out its new ChatGPT-4o model in May 2024, bringing major performance upgrades over GPT-4 Turbo. The company claimed it was twice as fast, offered five times the usage limits, and cost half as much. What set ChatGPT-4o apart was its ability to work seamlessly across text, audio, and now images – making it multimodal. It can tackle complex math, interpret facial expressions, and translate spoken conversations instantly. With the recent addition of image generation tool directly on the GPT-4o AI model, users can now also interact across all major formats in a more intuitive, human-like way.
