NEWS
Social shopping-focused Chums announces $3.5M raise ahead of YC Demo Day
With Y Combinator Demo Day kicking off tomorrow morning, startups in the current batch are hurrying to make a little news before they show off their recent growth to investors. The list includes Runway, Mono, Pangea and Flux.
Add Chums to the mix. Chums is a social shopping service that helps friends suggest products to their pals. And the startup has put together a total of $3.5 million across two pre-seed investments.
TechCrunch spoke with Noah Elion, one of Chums’ founders, about the round. He said that his company closed $1 million in December, later looking to raise another $1.5 million. Interest ran high for shares in the startup, so Chums wound up raising $1 million more than its latter target, for a combined total of $3.5 million.
The company declined to share the cap at which the funds, raised via a SAFE, were secured.
The $1.5 million target was based on the amount of capital that his company would need for the next 18 months, Elion said. The final sum came from Ludlow, Shrug, Contrary Capital and Fuel Capital, among other firms and individuals.
How did a company in the midst of Y Combinator manage to raise an old-school Series A round of capital despite launching its product just a few weeks ago? The background of its founding team helps some. Co-founder Dick Fickling was an early engineer at Honey, for example, another shopping-focused startup that had a material exit.
The startup’s service is a mobile app that allows users to follow product-types that they may want to purchase, and suggest goods to one another that might fit their friends’ needs. It made its way to market three weeks ago, or as Elion explained, right before his company went out fundraising. TechCrunch asked about early traction, to which Elion said that it was too soon to say much, though his team has seen “encouraging” levels of engagement thus far.
The startup is four people today, which its website describes as a group of friends. This is mostly true. Elion and Fickling teamed up after the former built a predecessor to Chums — called Chums Referral — becoming friends in the process. Fickling was previously colleagues and friends with the folks who comprise the rest of the team, namely Lauren Williams (director of engineering) and Lena Gasilina (product).
The team is looking for a designer and a front-end developer, but after that is done hiring. It intends to stay at six people until its next round. Why? It wants to reach product-market fit with a half-dozen staff. If it does, it should be able to raise more money at a comfortable valuation. There’s some sense in the idea, though it was slightly odd to hear a startup plan measured growth to preserve capital in 2021.
Chums makes money on commissions from recommended products, splitting the revenue with users. Elion declined to share the network, or networks, his company is working with to secure commercial ties with retailers, but did note that in time Chums will go direct to secure better deals.
With a closed round, most of its team in place and an app in the market, it’s now up to Chums to prove Elion’s view Google is overly gamed and Amazon is best when you know what you are looking for. In the co-founder’s view, people liked malls for their “diversity of content” and as a space for “spontaneous shopping.” Perhaps Chums can fit that niche, and, in the process, generate some serious coin.
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NEWS
OpenAI Introduces Fine-Tuning for GPT-4 and Enabling Customized AI Models
OpenAI has today announced the release of fine-tuning capabilities for its flagship GPT-4 large language model, marking a significant milestone in the AI landscape. This new functionality empowers developers to create tailored versions of GPT-4 to suit specialized use cases, enhancing the model’s utility across various industries.
Fine-tuning has long been a desired feature for developers who require more control over AI behavior, and with this update, OpenAI delivers on that demand. The ability to fine-tune GPT-4 allows businesses and developers to refine the model’s responses to better align with specific requirements, whether for customer service, content generation, technical support, or other unique applications.
Why Fine-Tuning Matters
GPT-4 is a very flexible model that can handle many different tasks. However, some businesses and developers need more specialized AI that matches their specific language, style, and needs. Fine-tuning helps with this by letting them adjust GPT-4 using custom data. For example, companies can train a fine-tuned model to keep a consistent brand tone or focus on industry-specific language.
Fine-tuning also offers improvements in areas like response accuracy and context comprehension. For use cases where nuanced understanding or specialized knowledge is crucial, this can be a game-changer. Models can be taught to better grasp intricate details, improving their effectiveness in sectors such as legal analysis, medical advice, or technical writing.
Key Features of GPT-4 Fine-Tuning
The fine-tuning process leverages OpenAI’s established tools, but now it is optimized for GPT-4’s advanced architecture. Notable features include:
- Enhanced Customization: Developers can precisely influence the model’s behavior and knowledge base.
- Consistency in Output: Fine-tuned models can be made to maintain consistent formatting, tone, or responses, essential for professional applications.
- Higher Efficiency: Compared to training models from scratch, fine-tuning GPT-4 allows organizations to deploy sophisticated AI with reduced time and computational cost.
Additionally, OpenAI has emphasized ease of use with this feature. The fine-tuning workflow is designed to be accessible even to teams with limited AI experience, reducing barriers to customization. For more advanced users, OpenAI provides granular control options to achieve highly specialized outputs.
Implications for the Future
The launch of fine-tuning capabilities for GPT-4 signals a broader shift toward more user-centric AI development. As businesses increasingly adopt AI, the demand for models that can cater to specific business needs, without compromising on performance, will continue to grow. OpenAI’s move positions GPT-4 as a flexible and adaptable tool that can be refined to deliver optimal value in any given scenario.
By offering fine-tuning, OpenAI not only enhances GPT-4’s appeal but also reinforces the model’s role as a leading AI solution across diverse sectors. From startups seeking to automate niche tasks to large enterprises looking to scale intelligent systems, GPT-4’s fine-tuning capability provides a powerful resource for driving innovation.
OpenAI announced that fine-tuning GPT-4o will cost $25 for every million tokens used during training. After the model is set up, it will cost $3.75 per million input tokens and $15 per million output tokens. To help developers get started, OpenAI is offering 1 million free training tokens per day for GPT-4o and 2 million free tokens per day for GPT-4o mini until September 23. This makes it easier for developers to try out the fine-tuning service.
As AI continues to evolve, OpenAI’s focus on customization and adaptability with GPT-4 represents a critical step in making advanced AI accessible, scalable, and more aligned with real-world applications. This new capability is expected to accelerate the adoption of AI across industries, creating a new wave of AI-driven solutions tailored to specific challenges and opportunities.
This Week in Search News: Simple and Easy-to-Read Update
Here’s what happened in the world of Google and search engines this week:
1. Google’s June 2024 Spam Update
Google finished rolling out its June 2024 spam update over a period of seven days. This update aims to reduce spammy content in search results.
2. Changes to Google Search Interface
Google has removed the continuous scroll feature for search results. Instead, it’s back to the old system of pages.
3. New Features and Tests
- Link Cards: Google is testing link cards at the top of AI-generated overviews.
- Health Overviews: There are more AI-generated health overviews showing up in search results.
- Local Panels: Google is testing AI overviews in local information panels.
4. Search Rankings and Quality
- Improving Rankings: Google said it can improve its search ranking system but will only do so on a large scale.
- Measuring Quality: Google’s Elizabeth Tucker shared how they measure search quality.
5. Advice for Content Creators
- Brand Names in Reviews: Google advises not to avoid mentioning brand names in review content.
- Fixing 404 Pages: Google explained when it’s important to fix 404 error pages.
6. New Search Features in Google Chrome
Google Chrome for mobile devices has added several new search features to enhance user experience.
7. New Tests and Features in Google Search
- Credit Card Widget: Google is testing a new widget for credit card information in search results.
- Sliding Search Results: When making a new search query, the results might slide to the right.
8. Bing’s New Feature
Bing is now using AI to write “People Also Ask” questions in search results.
9. Local Search Ranking Factors
Menu items and popular times might be factors that influence local search rankings on Google.
10. Google Ads Updates
- Query Matching and Brand Controls: Google Ads updated its query matching and brand controls, and advertisers are happy with these changes.
- Lead Credits: Google will automate lead credits for Local Service Ads. Google says this is a good change, but some advertisers are worried.
- tROAS Insights Box: Google Ads is testing a new insights box for tROAS (Target Return on Ad Spend) in Performance Max and Standard Shopping campaigns.
- WordPress Tag Code: There is a new conversion code for Google Ads on WordPress sites.
These updates highlight how Google and other search engines are continuously evolving to improve user experience and provide better advertising tools.
Facebook Faces Yet Another Outage: Platform Encounters Technical Issues Again
Uppdated: It seems that today’s issues with Facebook haven’t affected as many users as the last time. A smaller group of people appears to be impacted this time around, which is a relief compared to the larger incident before. Nevertheless, it’s still frustrating for those affected, and hopefully, the issues will be resolved soon by the Facebook team.
Facebook had another problem today (March 20, 2024). According to Downdetector, a website that shows when other websites are not working, many people had trouble using Facebook.
This isn’t the first time Facebook has had issues. Just a little while ago, there was another problem that stopped people from using the site. Today, when people tried to use Facebook, it didn’t work like it should. People couldn’t see their friends’ posts, and sometimes the website wouldn’t even load.
Downdetector, which watches out for problems on websites, showed that lots of people were having trouble with Facebook. People from all over the world said they couldn’t use the site, and they were not happy about it.
When websites like Facebook have problems, it affects a lot of people. It’s not just about not being able to see posts or chat with friends. It can also impact businesses that use Facebook to reach customers.
Since Facebook owns Messenger and Instagram, the problems with Facebook also meant that people had trouble using these apps. It made the situation even more frustrating for many users, who rely on these apps to stay connected with others.
During this recent problem, one thing is obvious: the internet is always changing, and even big websites like Facebook can have problems. While people wait for Facebook to fix the issue, it shows us how easily things online can go wrong. It’s a good reminder that we should have backup plans for staying connected online, just in case something like this happens again.
